DIABETIC DATASET

encounter_id	patient_nbr	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	medical_specialty	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
2278392	8222157	Caucasian	Female	[0-10)	?	6	25	1	1	?	Pediatrics-Endocrinology	41	0	1	0	0	250.83	?	?	1	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO
149190	55629189	Caucasian	Female	[10-20)	?	1	1	7	3	?	?	59	0	18	0	0	276	250.01	255	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	>30
64410	86047875	AfricanAmerican	Female	[20-30)	?	1	1	7	2	?	?	11	5	13	2	1	648	250	V27	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO
500364	82442376	Caucasian	Male	[30-40)	?	1	1	7	2	?	?	44	1	16	0	0	8	250.43	403	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO
16680	42519267	Caucasian	Male	[40-50)	?	1	1	7	1	?	?	51	0	8	0	0	197	157	250	5	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO
35754	82637451	Caucasian	Male	[50-60)	?	2	1	2	3	?	?	31	6	16	0	0	414	411	250	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30

DATA PRE-PROCESSING

Checking for the missing values

summary(diabetic_datadup)

  encounter_id        patient_nbr                     race      
 Min.   :    12522   Min.   :      135   ?              : 2273  
 1st Qu.: 84961194   1st Qu.: 23413221   AfricanAmerican:19210  
 Median :152388987   Median : 45505143   Asian          :  641  
 Mean   :165201646   Mean   : 54330401   Caucasian      :76099  
 3rd Qu.:230270888   3rd Qu.: 87545950   Hispanic       : 2037  
 Max.   :443867222   Max.   :189502619   Other          : 1506  
                                                                
             gender           age              weight     
 Female         :54708   [70-80):26068   ?        :98569  
 Male           :47055   [60-70):22483   [75-100) : 1336  
 Unknown/Invalid:    3   [50-60):17256   [50-75)  :  897  
                         [80-90):17197   [100-125):  625  
                         [40-50): 9685   [125-150):  145  
                         [30-40): 3775   [25-50)  :   97  
                         (Other): 5302   (Other)  :   97  
 admission_type_id discharge_disposition_id admission_source_id
 Min.   :1.000     Min.   : 1.000           Min.   : 1.000     
 1st Qu.:1.000     1st Qu.: 1.000           1st Qu.: 1.000     
 Median :1.000     Median : 1.000           Median : 7.000     
 Mean   :2.024     Mean   : 3.716           Mean   : 5.754     
 3rd Qu.:3.000     3rd Qu.: 4.000           3rd Qu.: 7.000     
 Max.   :8.000     Max.   :28.000           Max.   :25.000     
                                                               
 time_in_hospital   payer_code                 medical_specialty
 Min.   : 1.000   ?      :40256   ?                     :49949  
 1st Qu.: 2.000   MC     :32439   InternalMedicine      :14635  
 Median : 4.000   HM     : 6274   Emergency/Trauma      : 7565  
 Mean   : 4.396   SP     : 5007   Family/GeneralPractice: 7440  
 3rd Qu.: 6.000   BC     : 4655   Cardiology            : 5352  
 Max.   :14.000   MD     : 3532   Surgery-General       : 3099  
                  (Other): 9603   (Other)               :13726  
 num_lab_procedures num_procedures num_medications number_outpatient
 Min.   :  1.0      Min.   :0.00   Min.   : 1.00   Min.   : 0.0000  
 1st Qu.: 31.0      1st Qu.:0.00   1st Qu.:10.00   1st Qu.: 0.0000  
 Median : 44.0      Median :1.00   Median :15.00   Median : 0.0000  
 Mean   : 43.1      Mean   :1.34   Mean   :16.02   Mean   : 0.3694  
 3rd Qu.: 57.0      3rd Qu.:2.00   3rd Qu.:20.00   3rd Qu.: 0.0000  
 Max.   :132.0      Max.   :6.00   Max.   :81.00   Max.   :42.0000  
                                                                    
 number_emergency  number_inpatient      diag_1          diag_2     
 Min.   : 0.0000   Min.   : 0.0000   428    : 6862   276    : 6752  
 1st Qu.: 0.0000   1st Qu.: 0.0000   414    : 6581   428    : 6662  
 Median : 0.0000   Median : 0.0000   786    : 4016   250    : 6071  
 Mean   : 0.1978   Mean   : 0.6356   410    : 3614   427    : 5036  
 3rd Qu.: 0.0000   3rd Qu.: 1.0000   486    : 3508   401    : 3736  
 Max.   :76.0000   Max.   :21.0000   427    : 2766   496    : 3305  
                                     (Other):74419   (Other):70204  
     diag_3      number_diagnoses max_glu_serum A1Cresult   
 250    :11555   Min.   : 1.000   >200: 1485    >7  : 3812  
 401    : 8289   1st Qu.: 6.000   >300: 1264    >8  : 8216  
 276    : 5175   Median : 8.000   None:96420    None:84748  
 428    : 4577   Mean   : 7.423   Norm: 2597    Norm: 4990  
 427    : 3955   3rd Qu.: 9.000                             
 414    : 3664   Max.   :16.000                             
 (Other):64551                                              
  metformin     repaglinide     nateglinide     chlorpropamide 
 Down  :  575   Down  :    45   Down  :    11   Down  :     1  
 No    :81778   No    :100227   No    :101063   No    :101680  
 Steady:18346   Steady:  1384   Steady:   668   Steady:    79  
 Up    : 1067   Up    :   110   Up    :    24   Up    :     6  
                                                               
                                                               
                                                               
 glimepiride    acetohexamide    glipizide      glyburide    
 Down  :  194   No    :101765   Down  :  560   Down  :  564  
 No    :96575   Steady:     1   No    :89080   No    :91116  
 Steady: 4670                   Steady:11356   Steady: 9274  
 Up    :  327                   Up    :  770   Up    :  812  
                                                             
                                                             
                                                             
 tolbutamide     pioglitazone   rosiglitazone    acarbose     
 No    :101743   Down  :  118   Down  :   87   Down  :     3  
 Steady:    23   No    :94438   No    :95401   No    :101458  
                 Steady: 6976   Steady: 6100   Steady:   295  
                 Up    :  234   Up    :  178   Up    :    10  
                                                              
                                                              
                                                              
   miglitol      troglitazone     tolazamide     examide     citoglipton
 Down  :     5   No    :101763   No    :101727   No:101766   No:101766  
 No    :101728   Steady:     3   Steady:    38                          
 Steady:    31                   Up    :     1                          
 Up    :     2                                                          
                                                                        
                                                                        
                                                                        
   insulin      glyburide.metformin glipizide.metformin
 Down  :12218   Down  :     6       No    :101753      
 No    :47383   No    :101060       Steady:    13      
 Steady:30849   Steady:   692                          
 Up    :11316   Up    :     8                          
                                                       
                                                       
                                                       
 glimepiride.pioglitazone metformin.rosiglitazone metformin.pioglitazone
 No    :101765            No    :101764           No    :101765         
 Steady:     1            Steady:     2           Steady:     1         
                                                                        
                                                                        
                                                                        
                                                                        
                                                                        
 change     diabetesMed readmitted 
 Ch:47011   No :23403   <30:11357  
 No:54755   Yes:78363   >30:35545  
                        NO :54864

 Here,in the dataset there are missing values in certain variables,as we can see missing values in variable 'race' which are 2273(i.e. nearly 2%) we shouldnt exclude the race variable.
 But in variable 'weight' there are 98569 missing values which accounts to nearly 97% which clearly indicates that the data is not sufficient in weight variable, so we have to exclude it.
 Similarly, in variable 'payer code' it has 40256 missing values (i.e.nearly 40%) so we can exclude it.
 Similarly in variable 'medical_specialty' there are 49949 missing values (i.e. nearly 49%)so we can exclude this variable.

Removing insignificant variables

diabetic_datadup<-subset(diabetic_datadup,select = -c(weight,payer_code,medical_specialty))
View(diabetic_datadup)
summary(diabetic_datadup)

  encounter_id        patient_nbr                     race      
 Min.   :    12522   Min.   :      135   ?              : 2273  
 1st Qu.: 84961194   1st Qu.: 23413221   AfricanAmerican:19210  
 Median :152388987   Median : 45505143   Asian          :  641  
 Mean   :165201646   Mean   : 54330401   Caucasian      :76099  
 3rd Qu.:230270888   3rd Qu.: 87545950   Hispanic       : 2037  
 Max.   :443867222   Max.   :189502619   Other          : 1506  
                                                                
             gender           age        admission_type_id
 Female         :54708   [70-80):26068   Min.   :1.000    
 Male           :47055   [60-70):22483   1st Qu.:1.000    
 Unknown/Invalid:    3   [50-60):17256   Median :1.000    
                         [80-90):17197   Mean   :2.024    
                         [40-50): 9685   3rd Qu.:3.000    
                         [30-40): 3775   Max.   :8.000    
                         (Other): 5302                    
 discharge_disposition_id admission_source_id time_in_hospital
 Min.   : 1.000           Min.   : 1.000      Min.   : 1.000  
 1st Qu.: 1.000           1st Qu.: 1.000      1st Qu.: 2.000  
 Median : 1.000           Median : 7.000      Median : 4.000  
 Mean   : 3.716           Mean   : 5.754      Mean   : 4.396  
 3rd Qu.: 4.000           3rd Qu.: 7.000      3rd Qu.: 6.000  
 Max.   :28.000           Max.   :25.000      Max.   :14.000  
                                                              
 num_lab_procedures num_procedures num_medications number_outpatient
 Min.   :  1.0      Min.   :0.00   Min.   : 1.00   Min.   : 0.0000  
 1st Qu.: 31.0      1st Qu.:0.00   1st Qu.:10.00   1st Qu.: 0.0000  
 Median : 44.0      Median :1.00   Median :15.00   Median : 0.0000  
 Mean   : 43.1      Mean   :1.34   Mean   :16.02   Mean   : 0.3694  
 3rd Qu.: 57.0      3rd Qu.:2.00   3rd Qu.:20.00   3rd Qu.: 0.0000  
 Max.   :132.0      Max.   :6.00   Max.   :81.00   Max.   :42.0000  
                                                                    
 number_emergency  number_inpatient      diag_1          diag_2     
 Min.   : 0.0000   Min.   : 0.0000   428    : 6862   276    : 6752  
 1st Qu.: 0.0000   1st Qu.: 0.0000   414    : 6581   428    : 6662  
 Median : 0.0000   Median : 0.0000   786    : 4016   250    : 6071  
 Mean   : 0.1978   Mean   : 0.6356   410    : 3614   427    : 5036  
 3rd Qu.: 0.0000   3rd Qu.: 1.0000   486    : 3508   401    : 3736  
 Max.   :76.0000   Max.   :21.0000   427    : 2766   496    : 3305  
                                     (Other):74419   (Other):70204  
     diag_3      number_diagnoses max_glu_serum A1Cresult   
 250    :11555   Min.   : 1.000   >200: 1485    >7  : 3812  
 401    : 8289   1st Qu.: 6.000   >300: 1264    >8  : 8216  
 276    : 5175   Median : 8.000   None:96420    None:84748  
 428    : 4577   Mean   : 7.423   Norm: 2597    Norm: 4990  
 427    : 3955   3rd Qu.: 9.000                             
 414    : 3664   Max.   :16.000                             
 (Other):64551                                              
  metformin     repaglinide     nateglinide     chlorpropamide 
 Down  :  575   Down  :    45   Down  :    11   Down  :     1  
 No    :81778   No    :100227   No    :101063   No    :101680  
 Steady:18346   Steady:  1384   Steady:   668   Steady:    79  
 Up    : 1067   Up    :   110   Up    :    24   Up    :     6  
                                                               
                                                               
                                                               
 glimepiride    acetohexamide    glipizide      glyburide    
 Down  :  194   No    :101765   Down  :  560   Down  :  564  
 No    :96575   Steady:     1   No    :89080   No    :91116  
 Steady: 4670                   Steady:11356   Steady: 9274  
 Up    :  327                   Up    :  770   Up    :  812  
                                                             
                                                             
                                                             
 tolbutamide     pioglitazone   rosiglitazone    acarbose     
 No    :101743   Down  :  118   Down  :   87   Down  :     3  
 Steady:    23   No    :94438   No    :95401   No    :101458  
                 Steady: 6976   Steady: 6100   Steady:   295  
                 Up    :  234   Up    :  178   Up    :    10  
                                                              
                                                              
                                                              
   miglitol      troglitazone     tolazamide     examide     citoglipton
 Down  :     5   No    :101763   No    :101727   No:101766   No:101766  
 No    :101728   Steady:     3   Steady:    38                          
 Steady:    31                   Up    :     1                          
 Up    :     2                                                          
                                                                        
                                                                        
                                                                        
   insulin      glyburide.metformin glipizide.metformin
 Down  :12218   Down  :     6       No    :101753      
 No    :47383   No    :101060       Steady:    13      
 Steady:30849   Steady:   692                          
 Up    :11316   Up    :     8                          
                                                       
                                                       
                                                       
 glimepiride.pioglitazone metformin.rosiglitazone metformin.pioglitazone
 No    :101765            No    :101764           No    :101765         
 Steady:     1            Steady:     2           Steady:     1         
                                                                        
                                                                        
                                                                        
                                                                        
                                                                        
 change     diabetesMed readmitted 
 Ch:47011   No :23403   <30:11357  
 No:54755   Yes:78363   >30:35545  
                        NO :54864

   Hence, the unwanted variables are ejected from the dataset.

Replacing missing values

class(diabetic_datadup$race)

[1] "factor"

diabetic_datadup$race<-as.character(diabetic_datadup$race)
class(diabetic_datadup$race)

[1] "character"

diabetic_datadup$race<-ifelse(diabetic_datadup$race=="?","Caucasian",diabetic_datadup$race)
View(diabetic_datadup)
summary(diabetic_datadup)

  encounter_id        patient_nbr            race          
 Min.   :    12522   Min.   :      135   Length:101766     
 1st Qu.: 84961194   1st Qu.: 23413221   Class :character  
 Median :152388987   Median : 45505143   Mode  :character  
 Mean   :165201646   Mean   : 54330401                     
 3rd Qu.:230270888   3rd Qu.: 87545950                     
 Max.   :443867222   Max.   :189502619                     
                                                           
             gender           age        admission_type_id
 Female         :54708   [70-80):26068   Min.   :1.000    
 Male           :47055   [60-70):22483   1st Qu.:1.000    
 Unknown/Invalid:    3   [50-60):17256   Median :1.000    
                         [80-90):17197   Mean   :2.024    
                         [40-50): 9685   3rd Qu.:3.000    
                         [30-40): 3775   Max.   :8.000    
                         (Other): 5302                    
 discharge_disposition_id admission_source_id time_in_hospital
 Min.   : 1.000           Min.   : 1.000      Min.   : 1.000  
 1st Qu.: 1.000           1st Qu.: 1.000      1st Qu.: 2.000  
 Median : 1.000           Median : 7.000      Median : 4.000  
 Mean   : 3.716           Mean   : 5.754      Mean   : 4.396  
 3rd Qu.: 4.000           3rd Qu.: 7.000      3rd Qu.: 6.000  
 Max.   :28.000           Max.   :25.000      Max.   :14.000  
                                                              
 num_lab_procedures num_procedures num_medications number_outpatient
 Min.   :  1.0      Min.   :0.00   Min.   : 1.00   Min.   : 0.0000  
 1st Qu.: 31.0      1st Qu.:0.00   1st Qu.:10.00   1st Qu.: 0.0000  
 Median : 44.0      Median :1.00   Median :15.00   Median : 0.0000  
 Mean   : 43.1      Mean   :1.34   Mean   :16.02   Mean   : 0.3694  
 3rd Qu.: 57.0      3rd Qu.:2.00   3rd Qu.:20.00   3rd Qu.: 0.0000  
 Max.   :132.0      Max.   :6.00   Max.   :81.00   Max.   :42.0000  
                                                                    
 number_emergency  number_inpatient      diag_1          diag_2     
 Min.   : 0.0000   Min.   : 0.0000   428    : 6862   276    : 6752  
 1st Qu.: 0.0000   1st Qu.: 0.0000   414    : 6581   428    : 6662  
 Median : 0.0000   Median : 0.0000   786    : 4016   250    : 6071  
 Mean   : 0.1978   Mean   : 0.6356   410    : 3614   427    : 5036  
 3rd Qu.: 0.0000   3rd Qu.: 1.0000   486    : 3508   401    : 3736  
 Max.   :76.0000   Max.   :21.0000   427    : 2766   496    : 3305  
                                     (Other):74419   (Other):70204  
     diag_3      number_diagnoses max_glu_serum A1Cresult   
 250    :11555   Min.   : 1.000   >200: 1485    >7  : 3812  
 401    : 8289   1st Qu.: 6.000   >300: 1264    >8  : 8216  
 276    : 5175   Median : 8.000   None:96420    None:84748  
 428    : 4577   Mean   : 7.423   Norm: 2597    Norm: 4990  
 427    : 3955   3rd Qu.: 9.000                             
 414    : 3664   Max.   :16.000                             
 (Other):64551                                              
  metformin     repaglinide     nateglinide     chlorpropamide 
 Down  :  575   Down  :    45   Down  :    11   Down  :     1  
 No    :81778   No    :100227   No    :101063   No    :101680  
 Steady:18346   Steady:  1384   Steady:   668   Steady:    79  
 Up    : 1067   Up    :   110   Up    :    24   Up    :     6  
                                                               
                                                               
                                                               
 glimepiride    acetohexamide    glipizide      glyburide    
 Down  :  194   No    :101765   Down  :  560   Down  :  564  
 No    :96575   Steady:     1   No    :89080   No    :91116  
 Steady: 4670                   Steady:11356   Steady: 9274  
 Up    :  327                   Up    :  770   Up    :  812  
                                                             
                                                             
                                                             
 tolbutamide     pioglitazone   rosiglitazone    acarbose     
 No    :101743   Down  :  118   Down  :   87   Down  :     3  
 Steady:    23   No    :94438   No    :95401   No    :101458  
                 Steady: 6976   Steady: 6100   Steady:   295  
                 Up    :  234   Up    :  178   Up    :    10  
                                                              
                                                              
                                                              
   miglitol      troglitazone     tolazamide     examide     citoglipton
 Down  :     5   No    :101763   No    :101727   No:101766   No:101766  
 No    :101728   Steady:     3   Steady:    38                          
 Steady:    31                   Up    :     1                          
 Up    :     2                                                          
                                                                        
                                                                        
                                                                        
   insulin      glyburide.metformin glipizide.metformin
 Down  :12218   Down  :     6       No    :101753      
 No    :47383   No    :101060       Steady:    13      
 Steady:30849   Steady:   692                          
 Up    :11316   Up    :     8                          
                                                       
                                                       
                                                       
 glimepiride.pioglitazone metformin.rosiglitazone metformin.pioglitazone
 No    :101765            No    :101764           No    :101765         
 Steady:     1            Steady:     2           Steady:     1         
                                                                        
                                                                        
                                                                        
                                                                        
                                                                        
 change     diabetesMed readmitted 
 Ch:47011   No :23403   <30:11357  
 No:54755   Yes:78363   >30:35545  
                        NO :54864

  Here,the missing values in the variable 'race' are to be properly replaced by some value, as we have more 'caucasian' classes (nearly 78372 values) we will replace the missing values with the caucasian class.
  After replacing the missing values check if still there are any missing values,here we can see that no missing values available.

BUILD THE MODEL

library(naivebayes)
model<-naive_bayes(readmitted ~ ., data=diabetic_datadup)
model

===================== Naive Bayes ===================== 
Call: 
naive_bayes.formula(formula = readmitted ~ ., data = diabetic_datadup)

A priori probabilities: 

      <30       >30        NO 
0.1115992 0.3492817 0.5391192 

Tables: 
            
encounter_id       <30       >30        NO
        mean 162734709 160384538 168833191
        sd   101777344  96212488 106632747

           
patient_nbr      <30      >30       NO
       mean 55192188 58146748 51679493
       sd   37804417 36614253 39956952

                 
race                      <30         >30          NO
  AfricanAmerican 0.189750814 0.186636658 0.189942403
  Asian           0.005723342 0.004529470 0.007564159
  Caucasian       0.773091485 0.778224785 0.764253427
  Hispanic        0.018666901 0.018061612 0.021562409
  Other           0.012767456 0.012547475 0.016677603

                 
gender                     <30          >30           NO
  Female          5.416923e-01 5.491068e-01 5.292724e-01
  Male            4.583077e-01 4.508932e-01 4.706729e-01
  Unknown/Invalid 0.000000e+00 0.000000e+00 5.468066e-05

          
age                 <30          >30           NO
  [0-10)   0.0002641543 0.0007314672 0.0024059493
  [10-20)  0.0035220569 0.0063018709 0.0077828813
  [20-30)  0.0207801356 0.0143480096 0.0166046952
  [30-40)  0.0373338029 0.0333942889 0.0394429863
  [40-50)  0.0904288104 0.0922211281 0.0980606591
  [50-60)  0.1468697719 0.1664650443 0.1762722368
  [60-70)  0.2203046579 0.2221690814 0.2202537183
  [70-80)  0.2702298142 0.2665635110 0.2465004374
  [80-90)  0.1829708550 0.1750738500 0.1621463984
  [90-100) 0.0272959408 0.0227317485 0.0305300379

# ... and 41 more tables

Here, out of many classification models only naive bayes model(probabilistic mmodel) was working on this dataset.

PREDICT THE OUTCOME

predicted_values<-predict(model,diabetic_datadup,type="class")
observed_values<-diabetic_datadup$readmitted
final<-data.frame(observed_values,predicted_values)

Confusion matrix

library(caret)

Loading required package: lattice

Loading required package: ggplot2

confusionMatrix(diabetic_datadup$readmitted,predicted_values)

Confusion Matrix and Statistics

          Reference
Prediction   <30   >30    NO
       <30  1455  2202  7700
       >30  2017  7800 25728
       NO   1282  4796 48786

Overall Statistics
                                          
               Accuracy : 0.5703          
                 95% CI : (0.5673, 0.5734)
    No Information Rate : 0.8079          
    P-Value [Acc > NIR] : 1               
                                          
                  Kappa : 0.155           
 Mcnemar's Test P-Value : <2e-16          

Statistics by Class:

                     Class: <30 Class: >30 Class: NO
Sensitivity             0.30606    0.52710    0.5934
Specificity             0.89793    0.68097    0.6891
Pos Pred Value          0.12811    0.21944    0.8892
Neg Pred Value          0.96351    0.89432    0.2873
Prevalence              0.04672    0.14541    0.8079
Detection Rate          0.01430    0.07665    0.4794
Detection Prevalence    0.11160    0.34928    0.5391
Balanced Accuracy       0.60199    0.60404    0.6413

 Here, we get contingency table and we can see that the accuracy is very less(57%) and from the cohens kappa value being very much less than 0.70 we interpret that inter-rater reliability is not satisfactory.
 And the true positive rate and true negative rate is also less.

Area under curve

library(pROC)

Type 'citation("pROC")' for a citation.


Attaching package: 'pROC'

The following objects are masked from 'package:stats':

    cov, smooth, var

library(InformationValue)


Attaching package: 'InformationValue'

The following objects are masked from 'package:caret':

    confusionMatrix, precision, sensitivity, specificity

multiclass.roc(as.numeric(diabetic_datadup$readmitted),as.numeric(predicted_values))


Call:
multiclass.roc.default(response = as.numeric(diabetic_datadup$readmitted),     predictor = as.numeric(predicted_values))

Data: as.numeric(predicted_values) with 3 levels of as.numeric(diabetic_datadup$readmitted): 1, 2, 3.
Multi-class area under the curve: 0.5743

area<-multiclass.roc(as.numeric(diabetic_datadup$readmitted),as.numeric(predicted_values),threshold = 0.5)
auc(area)

Multi-class area under the curve: 0.5743

 Here,the area under the curve is 57% which is very less and i think that this model is giving a bad performance.

FINAL DATASET

encounter_id	patient_nbr	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted	expected_readmitted
2278392	8222157	Caucasian	Female	[0-10)	6	25	1	1	41	0	1	0	0	0	250.83	?	?	1	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO	NO
149190	55629189	Caucasian	Female	[10-20)	1	1	7	3	59	0	18	0	0	0	276	250.01	255	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	>30	NO
64410	86047875	AfricanAmerican	Female	[20-30)	1	1	7	2	11	5	13	2	0	1	648	250	V27	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO	NO
500364	82442376	Caucasian	Male	[30-40)	1	1	7	2	44	1	16	0	0	0	8	250.43	403	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO	NO
16680	42519267	Caucasian	Male	[40-50)	1	1	7	1	51	0	8	0	0	0	197	157	250	5	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
35754	82637451	Caucasian	Male	[50-60)	2	1	2	3	31	6	16	0	0	0	414	411	250	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
55842	84259809	Caucasian	Male	[60-70)	3	1	2	4	70	1	21	0	0	0	414	411	V45	7	None	None	Steady	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
63768	114882984	Caucasian	Male	[70-80)	1	1	7	5	73	0	12	0	0	0	428	492	250	8	None	None	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	>30	NO
12522	48330783	Caucasian	Female	[80-90)	2	1	4	13	68	2	28	0	0	0	398	427	38	8	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
15738	63555939	Caucasian	Female	[90-100)	3	3	4	12	33	3	18	0	0	0	434	198	486	8	None	None	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
28236	89869032	AfricanAmerican	Female	[40-50)	1	1	7	9	47	2	17	0	0	0	250.7	403	996	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
36900	77391171	AfricanAmerican	Male	[60-70)	2	1	4	7	62	0	11	0	0	0	157	288	197	7	None	None	No	No	No	No	No	No	No	Up	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	<30	NO
40926	85504905	Caucasian	Female	[40-50)	1	3	7	7	60	0	15	0	1	0	428	250.43	250.6	8	None	None	Steady	Up	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	<30	>30
42570	77586282	Caucasian	Male	[80-90)	1	6	7	10	55	1	31	0	0	0	428	411	427	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO	NO
62256	49726791	AfricanAmerican	Female	[60-70)	3	1	2	1	49	5	2	0	0	0	518	998	627	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
73578	86328819	AfricanAmerican	Male	[60-70)	1	3	7	12	75	5	13	0	0	0	999	507	996	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO	NO
77076	92519352	AfricanAmerican	Male	[50-60)	1	1	7	4	45	4	17	0	0	0	410	411	414	8	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	<30	NO
84222	108662661	Caucasian	Female	[50-60)	1	1	7	3	29	0	11	0	0	0	682	174	250	3	None	None	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO	NO
89682	107389323	AfricanAmerican	Male	[70-80)	1	1	7	5	35	5	23	0	0	0	402	425	416	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
148530	69422211	Caucasian	Male	[70-80)	3	6	2	6	42	2	23	0	0	0	737	427	714	8	None	None	No	No	No	No	No	No	No	Down	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
150006	22864131	Caucasian	Female	[50-60)	2	1	4	2	66	1	19	0	0	0	410	427	428	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	NO	NO
150048	21239181	Caucasian	Male	[60-70)	2	1	4	2	36	2	11	0	0	0	572	456	427	6	None	None	Steady	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
182796	63000108	AfricanAmerican	Female	[70-80)	2	1	4	2	47	0	12	0	0	0	410	401	582	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO	NO
183930	107400762	Caucasian	Female	[80-90)	2	6	1	11	42	2	19	0	0	0	V57	715	V43	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	>30	NO
216156	62718876	AfricanAmerican	Female	[70-80)	3	1	2	3	19	4	18	0	0	0	189	496	427	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
221634	21861756	Other	Female	[50-60)	1	1	7	1	33	0	7	0	0	0	786	401	250	3	None	None	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO	NO
236316	40523301	Caucasian	Male	[80-90)	1	3	7	6	64	3	18	0	0	0	427	428	414	7	None	>7	Steady	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Ch	Yes	NO	NO
248916	115196778	Caucasian	Female	[50-60)	1	1	1	2	25	2	11	0	0	0	996	585	250.01	3	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
250872	41606064	Caucasian	Male	[20-30)	2	1	2	10	53	0	20	0	0	0	277	250.02	263	6	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	>30	NO
252822	18196434	Caucasian	Female	[80-90)	1	2	7	5	52	0	14	0	0	0	428	410	414	8	None	None	Steady	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Ch	Yes	>30	NO
253380	56480238	AfricanAmerican	Female	[60-70)	1	1	7	6	87	0	18	0	0	0	584	496	250.42	9	None	None	No	No	No	No	No	No	No	Up	No	No	No	Steady	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
253722	96664626	AfricanAmerican	Male	[70-80)	1	5	7	1	53	0	10	0	0	0	462	250.01	276	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	>30	NO
260166	80845353	Caucasian	Female	[70-80)	1	1	7	6	27	0	16	0	0	0	996	999	250.01	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
293058	114715242	Caucasian	Male	[60-70)	2	6	2	5	37	0	18	0	0	0	473	996	482	8	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
293118	3327282	Caucasian	Female	[70-80)	2	11	2	5	46	2	20	0	0	0	428	585	414	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	NO	NO
325848	63023292	Caucasian	Female	[60-70)	1	1	7	2	41	0	11	0	0	0	411	250.01	401	6	None	None	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	>30	NO
325866	98427861	Caucasian	Female	[70-80)	3	1	2	3	33	1	8	0	0	0	174	135	250	5	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO	NO
326028	112002975	Caucasian	Female	[60-70)	1	1	7	4	33	0	12	0	0	0	486	244	250	3	None	None	Steady	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	>30	NO
358776	101002446	Caucasian	Male	[70-80)	1	6	7	7	47	2	22	0	0	0	998	41	414	8	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
377268	104672268	Caucasian	Male	[50-60)	2	1	2	1	44	1	15	0	0	0	996	403	250.41	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
383430	80588529	Caucasian	Female	[70-80)	1	2	7	1	28	0	15	0	0	0	414	411	250.01	4	None	None	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	>30	NO
419304	99715041	Caucasian	Male	[40-50)	2	1	2	7	36	2	9	0	0	0	511	571	585	5	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30	NO
421194	96435585	Caucasian	Female	[70-80)	2	1	1	13	48	2	18	0	0	1	V57	276	781	8	None	None	Steady	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	>30	NO
449142	66274866	Caucasian	Male	[50-60)	1	1	7	3	59	0	11	0	0	0	428	496	278	6	None	None	Steady	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	>30	NO
450210	80177094	Caucasian	Female	[80-90)	1	11	7	7	72	1	27	0	0	0	432	997	427	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO	NO
464994	106936875	Caucasian	Female	[40-50)	3	1	2	2	10	3	8	0	0	0	626	250.01	998	5	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO	NO
486156	86240259	Caucasian	Female	[70-80)	3	5	4	9	25	3	16	0	0	2	428	427	250.01	7	None	None	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	<30	>30
498030	51838164	Caucasian	Male	[70-80)	3	3	4	9	2	0	12	0	0	1	295	599	250	3	None	None	Up	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO
537834	90097839	Caucasian	Male	[50-60)	3	1	2	6	65	5	19	0	0	0	414	424	428	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO	NO
544194	34997814	Caucasian	Male	[60-70)	2	6	4	11	67	2	25	0	0	0	428	491	427	9	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO	NO

DIABETIC

shekar

23 July 2018

DIABETIC DATASET

DATA PRE-PROCESSING

Checking for the missing values

Removing insignificant variables

Replacing missing values

BUILD THE MODEL

PREDICT THE OUTCOME

Confusion matrix

Area under curve

FINAL DATASET