summary_data <- select(measurements, id, userIds.0, data.indicator, data.context.symptoms.0)
summary_data_clean = na.omit(summary_data)
#Remove NA's
summary_data_clean <- summary_data[-which(summary_data$data.context.symptoms.0 == ""), ]
Removed_data = nrow(summary_data)-nrow(summary_data_clean)
#create a summary table
tabl <- table(summary_data$data.indicator, summary_data$data.context.symptoms.0)
tabl
##
## chest_pains confused fatigue lightheaded no_symptoms other
## normal 1260 109 4 1144 175 10935 332
## quality 370 84 9 226 58 2652 282
## urgent 809 162 26 1847 252 5539 615
## warning 250 22 0 224 40 1308 110
##
## palpitations shortness_of_breath
## normal 465 920
## quality 379 338
## urgent 1767 1541
## warning 134 188
### create AF column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.indicator[i] == "urgent")
{
summary_data_clean$data.indicator.af[i]= "1"
}
else
{
summary_data_clean$data.indicator.af[i]= "0"
}
}
### create Chestpains column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "chest_pains")
{
summary_data_clean$chestpains[i]= "1"
}
else
{
summary_data_clean$chestpains[i]= "0"
}
}
### create confused column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "confused")
{
summary_data_clean$confused[i]= "1"
}
else
{
summary_data_clean$confused[i]= "0"
}
}
### create fatigue column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "fatigue")
{
summary_data_clean$fatigue[i]= "1"
}
else
{
summary_data_clean$fatigue[i]= "0"
}
}
### create lightheaded column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "lightheaded")
{
summary_data_clean$lightheaded[i]= "1"
}
else
{
summary_data_clean$lightheaded[i]= "0"
}
}
### create no_symptoms column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "no_symptoms")
{
summary_data_clean$no_symptoms[i]= "1"
}
else
{
summary_data_clean$no_symptoms[i]= "0"
}
}
### create other column
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "other")
{
summary_data_clean$other[i]= "1"
}
else
{
summary_data_clean$other[i]= "0"
}
}
### palpitations
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "palpitations")
{
summary_data_clean$palpitations[i]= "1"
}
else
{
summary_data_clean$palpitations[i]= "0"
}
}
### shortness of breath
for(i in 1:nrow(summary_data_clean))
{
if(summary_data_clean$data.context.symptoms.0[i] == "shortness_of_breath")
{
summary_data_clean$shortness_of_breath[i]= "1"
}
else
{
summary_data_clean$shortness_of_breath[i]= "0"
}
}
summary_data_clean$data.indicator.af <- as.numeric(summary_data_clean$data.indicator.af)
summary_data_clean$chestpains <- as.numeric(summary_data_clean$chestpains)
summary_data_clean$palpitations <- as.numeric(summary_data_clean$palpitations)
summary_data_clean$shortness_of_breath <- as.numeric(summary_data_clean$shortness_of_breath)
summary_data_clean$other <- as.numeric(summary_data_clean$other)
summary_data_clean$lightheaded <- as.numeric(summary_data_clean$lightheaded)
summary_data_clean$confused <- as.numeric(summary_data_clean$confused)
summary_data_clean$no_symptoms <- as.numeric(summary_data_clean$no_symptoms)
summary_data_clean$lightheaded <- as.numeric(summary_data_clean$lightheaded)
summary_data_clean$fatigue <- as.numeric(summary_data_clean$fatigue)
##### chestpains
ROCit_obj_chestpain <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$chestpains)
ROCit_obj_chestpain
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 377
## Mean of positive(s): 0.4297
## Variance of positive(s): 0.2457
## ===== Negative(s) =====
## Number of negative(s): 31510
## Mean of negative(s): 0.3677
## Variance of negative(s): 0.2325
## ===== AUC =====
## Area under curve: 0.531
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3677 0.4297
## 1.0000 1.0000
plot(ROCit_obj_chestpain)
##### confused
ROCit_obj_confused <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$confused)
ROCit_obj_confused
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 39
## Mean of positive(s): 0.6667
## Variance of positive(s): 0.2281
## ===== Negative(s) =====
## Number of negative(s): 31848
## Mean of negative(s): 0.3681
## Variance of negative(s): 0.2326
## ===== AUC =====
## Area under curve: 0.6493
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3681 0.6667
## 1.0000 1.0000
plot(ROCit_obj_confused)
##### fatigue
ROCit_obj_fatigue <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$fatigue)
ROCit_obj_fatigue
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 3441
## Mean of positive(s): 0.5368
## Variance of positive(s): 0.2487
## ===== Negative(s) =====
## Number of negative(s): 28446
## Mean of negative(s): 0.3481
## Variance of negative(s): 0.2269
## ===== AUC =====
## Area under curve: 0.5943
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3481 0.5368
## 1.0000 1.0000
plot(ROCit_obj_fatigue)
##### lightheaded
ROCit_obj_lightheaded <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$lightheaded)
ROCit_obj_lightheaded
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 525
## Mean of positive(s): 0.48
## Variance of positive(s): 0.2501
## ===== Negative(s) =====
## Number of negative(s): 31362
## Mean of negative(s): 0.3666
## Variance of negative(s): 0.2322
## ===== AUC =====
## Area under curve: 0.5567
##
## =========================
## FPR TPR
## 0.0000 0.00
## 0.3666 0.48
## 1.0000 1.00
plot(ROCit_obj_lightheaded)
##### no_symptoms
ROCit_obj_no_symptoms <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$no_symptoms)
ROCit_obj_no_symptoms
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 20434
## Mean of positive(s): 0.2711
## Variance of positive(s): 0.1976
## ===== Negative(s) =====
## Number of negative(s): 11453
## Mean of negative(s): 0.5422
## Variance of negative(s): 0.2482
## ===== AUC =====
## Area under curve: 0.3644
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.5422 0.2711
## 1.0000 1.0000
plot(ROCit_obj_no_symptoms)
##### other
ROCit_obj_other <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$other)
ROCit_obj_other
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 1339
## Mean of positive(s): 0.4593
## Variance of positive(s): 0.2485
## ===== Negative(s) =====
## Number of negative(s): 30548
## Mean of negative(s): 0.3645
## Variance of negative(s): 0.2316
## ===== AUC =====
## Area under curve: 0.5474
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3645 0.4593
## 1.0000 1.0000
plot(ROCit_obj_other)
##### shortness_of_breath
ROCit_obj_shortness_of_breath <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$shortness_of_breath)
ROCit_obj_shortness_of_breath
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 2987
## Mean of positive(s): 0.5159
## Variance of positive(s): 0.2498
## ===== Negative(s) =====
## Number of negative(s): 28900
## Mean of negative(s): 0.3532
## Variance of negative(s): 0.2285
## ===== AUC =====
## Area under curve: 0.5813
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3532 0.5159
## 1.0000 1.0000
plot(ROCit_obj_shortness_of_breath)
##### palpitations
ROCit_obj_palpitations <- rocit(score=summary_data_clean$data.indicator.af,class=summary_data_clean$palpitations)
ROCit_obj_palpitations
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 2745
## Mean of positive(s): 0.6437
## Variance of positive(s): 0.2294
## ===== Negative(s) =====
## Number of negative(s): 29142
## Mean of negative(s): 0.3425
## Variance of negative(s): 0.2252
## ===== AUC =====
## Area under curve: 0.6506
##
## =========================
## FPR TPR
## 0.0000 0.0000
## 0.3425 0.6437
## 1.0000 1.0000
plot(ROCit_obj_palpitations)
