WEEK 14-FINAL PROJECT
Saisree Mucharla
2024-12-09
My final project focuses on finding the distinctions between those who are obese and those who are not. The project will be split into three primary parts: lifestyle habits, family history, and physical activity. The obesity dataset will be used in this research because it contains a variety of characteristics, such as family history, physical activity levels, and other lifestyle-related behaviors, which makes it perfect for determining the main distinctions between those who are obese and those who are not.
Obesity is a chronic condition characterized by excessive body fat, impacting over 42% of adults in the United States, as per CDC data. It is linked to severe health risks such as heart disease, type 2 diabetes, and some cancers.
key sources:
- Dataset Source: UCI Machine Learning Repository, includes 2111 records and 17 attributes.
- Demographic Variables: Age, gender.
- Behavioral Factors: Physical activity levels, frequency of vegetable consumption, family history of obesity, and more.
Dataset is loaded
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## The following object is masked from 'package:dplyr':
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## recodeobesity <- read.csv("C:/Users/ashir/Downloads/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition/ObesityDataSet_raw_and_data_sinthetic.csv")
# View the first few rows of the dataset
head(obesity)## Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP
## 1 Female 21 1.62 64.0 yes no 2 3
## 2 Female 21 1.52 56.0 yes no 3 3
## 3 Male 23 1.80 77.0 yes no 2 3
## 4 Male 27 1.80 87.0 no no 3 3
## 5 Male 22 1.78 89.8 no no 2 1
## 6 Male 29 1.62 53.0 no yes 2 3
## CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS
## 1 Sometimes no 2 no 0 1 no Public_Transportation
## 2 Sometimes yes 3 yes 3 0 Sometimes Public_Transportation
## 3 Sometimes no 2 no 2 1 Frequently Public_Transportation
## 4 Sometimes no 2 no 2 0 Frequently Walking
## 5 Sometimes no 2 no 0 0 Sometimes Public_Transportation
## 6 Sometimes no 2 no 0 0 Sometimes Automobile
## NObeyesdad
## 1 Normal_Weight
## 2 Normal_Weight
## 3 Normal_Weight
## 4 Overweight_Level_I
## 5 Overweight_Level_II
## 6 Normal_Weight# Convert categorical variables to factors
categorical_vars <- c("family_history_with_overweight", "FAVC", "CAEC",
"SMOKE", "SCC", "CALC", "MTRANS", "NObeyesdad")
obesity[categorical_vars] <- lapply(obesity[categorical_vars], as.factor)
# Create numeric BMI categories for regression
obesity_levels <- c("Insufficient_Weight" = 1,
"Normal_Weight" = 2,
"Overweight_Level_I" = 3,
"Overweight_Level_II" = 4,
"Obesity_Type_I" = 5,
"Obesity_Type_II" = 6,
"Obesity_Type_III" = 7)
obesity$BMI_level <- as.numeric(factor(obesity$NObeyesdad,
levels = names(obesity_levels),
labels = obesity_levels))Calculated BMI and added as new column
obesity$Weight <- as.numeric(as.character(obesity$Weight))
obesity$Height <- as.numeric(as.character(obesity$Height))
# Check for NA values
cat("NA in Weight:", sum(is.na(obesity$Weight_kg)), "\n")## NA in Weight: 0obesity <- na.omit(obesity)
# Calculate BMI
obesity$BMI <- obesity$Weight / (obesity$Height)
# Verify the calculation
head(obesity)## Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP
## 1 Female 21 1.62 64.0 yes no 2 3
## 2 Female 21 1.52 56.0 yes no 3 3
## 3 Male 23 1.80 77.0 yes no 2 3
## 4 Male 27 1.80 87.0 no no 3 3
## 5 Male 22 1.78 89.8 no no 2 1
## 6 Male 29 1.62 53.0 no yes 2 3
## CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS
## 1 Sometimes no 2 no 0 1 no Public_Transportation
## 2 Sometimes yes 3 yes 3 0 Sometimes Public_Transportation
## 3 Sometimes no 2 no 2 1 Frequently Public_Transportation
## 4 Sometimes no 2 no 2 0 Frequently Walking
## 5 Sometimes no 2 no 0 0 Sometimes Public_Transportation
## 6 Sometimes no 2 no 0 0 Sometimes Automobile
## NObeyesdad BMI_level BMI
## 1 Normal_Weight 2 39.50617
## 2 Normal_Weight 2 36.84211
## 3 Normal_Weight 2 42.77778
## 4 Overweight_Level_I 3 48.33333
## 5 Overweight_Level_II 4 50.44944
## 6 Normal_Weight 2 32.71605BMI Distribution
# Histogram for BMI
ggplot(obesity, aes(x = BMI)) +
geom_histogram(binwidth = 2, fill = "blue", color = "black") +
labs(title = "BMI Distribution", x = "BMI", y = "Count") +
theme_minimal()
Insights
- The BMI distribution shows a rather symmetrical trend with a rightward tail. This implies that although the majority of people have a modest BMI, some outliers have much higher numbers.
- There are multiple peaks in the histogram, indicating a multimodal distribution.
- A large number of individuals fall in the BMI range of 30–40 and 70–80, potentially indicating specific clusters of overweight or obese individuals.
- The distribution suggests variability, with certain BMI ranges (e.g., 20–30) having significantly fewer individuals.
- Outliers might be present at the extreme ends of the BMI scale.
Correlation Analysis
## Warning: package 'reshape2' was built under R version 4.4.2##
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## smiths# Calculate correlations
cor_matrix <- cor(obesity %>% select_if(is.numeric))
# Melt the correlation matrix into long format
cor_matrix_melted <- melt(cor_matrix)
# Create heatmap with ggplot2
ggplot(data = cor_matrix_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "red", high = "green", mid = "white", midpoint = 0) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Correlation Heatmap", x = "Variables", y = "Variables")
Insights
- Strong Correlations: BMI and Weight exhibit a strong positive correlation (dark green), as expected. Height and Age show a high correlation, potentially due to natural growth patterns.
- Negative Correlations: BMI and TUE (Time of Exercise) have a negative correlation, suggesting that higher exercise time may be associated with lower BMI.
- The strong relationship between BMI and Weight indicates that weight significantly contributes to BMI variation.
- The inverse relationship between BMI and exercise time highlights the importance of physical activity in maintaining a healthy BMI.
- Variables with weak correlations might require further examination or exclusion from predictive models.
Conclusion from these 2 visualizations
The study reveals a high frequency of overweight in the dataset and confirms established findings that activity and weight are important factors influencing BMI. Predictive models can be improved and targeted health interventions can be created with these findings.
Exploratory Data Analysis
so we have 2 hypotheses which have to be tested here.
Hypotheses 1: Higher levels of physical activity are associated with lower levels of obesity.
library(ggplot2)
# Box plot for Physical Activity vs Obesity
ggplot(obesity, aes(x=FAF, y=NObeyesdad, fill=NObeyesdad)) +
geom_boxplot() +
labs(title="Box Plot of BMI by Physical Activity Level",
x="Physical Activity Level", y="Obesity levels") +
theme_minimal() +
theme(legend.position = "none") 
Insights
- Insufficient Weight:
- This group has a wide spread in physical activity, with some individuals engaging in lower levels of activity and others at higher levels.
- The median physical activity level is slightly below 2, indicating moderate activity levels on average.
- Normal Weight:
- The distribution of physical activity is fairly consistent and slightly higher than Insufficient Weight.
- The median value hovers around 2, suggesting regular activity among this group.
- Overweight Levels I and II:
- Both categories display reduced physical activity compared to Normal Weight.
- The medians shift closer to 1.5, and the interquartile range narrows, showing that most individuals in this group fall into lower to moderate physical activity levels.
- Obesity Types I, II, III: As obesity severity increases:
- The median physical activity level drops further.
- The interquartile ranges shrink, indicating less variability in activity levels. For Obesity_Type_III, most individuals show physical activity levels close to 1, suggesting predominantly low activity.
chi-squared test for hypothesis-1
# Create a contingency table
table_physical_activity <- table(obesity$FAF, obesity$NObeyesdad)
# View the contingency table
table_physical_activity##
## Insufficient_Weight Normal_Weight Obesity_Type_I Obesity_Type_II
## 0 34 80 81 28
## 9.6e-05 0 0 0 0
## 0.000272 0 0 0 0
## 0.000454 0 0 0 0
## 0.001015 0 0 0 0
## 0.001086 0 0 1 0
## 0.001272 0 0 0 0
## 0.001297 0 0 0 0
## 0.00203 0 0 1 0
## 0.00342 0 0 0 0
## 0.003938 0 0 1 0
## 0.005405 0 0 1 0
## 0.005939 0 0 0 0
## 0.006265 0 0 0 0
## 0.00705 0 0 0 0
## 0.008013 0 0 0 0
## 0.008127 0 0 0 0
## 0.008368 0 0 1 0
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## 0.02006 0 0 0 0
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## 2.89118 0 0 1 0
## 2.891986 0 0 1 0
## 2.892922 0 0 0 0
## 2.931527 0 0 0 0
## 2.936551 0 0 0 0
## 2.939733 0 0 0 0
## 2.971832 0 0 0 0
## 2.998981 0 0 1 0
## 2.999918 0 0 1 0
## 3 4 41 13 0
##
## Obesity_Type_III Overweight_Level_I Overweight_Level_II
## 0 87 40 61
## 9.6e-05 1 0 0
## 0.000272 1 0 0
## 0.000454 1 0 0
## 0.001015 1 0 0
## 0.001086 0 0 0
## 0.001272 1 0 0
## 0.001297 1 0 0
## 0.00203 0 0 0
## 0.00342 0 1 0
## 0.003938 0 0 0
## 0.005405 0 0 0
## 0.005939 1 0 0
## 0.006265 1 0 0
## 0.00705 0 1 1
## 0.008013 1 0 0
## 0.008127 1 0 0
## 0.008368 0 0 0
## 0.010183 1 0 0
## 0.011161 1 0 0
## 0.011477 0 0 0
## 0.011519 0 0 0
## 0.01437 1 0 0
## 0.01586 2 0 0
## 0.01682 1 0 0
## 0.017804 1 0 0
## 0.019404 1 0 0
## 0.02006 1 0 0
## 0.02112 0 0 1
## 0.022598 0 0 0
## 0.022958 0 0 1
## 0.023574 0 0 0
## 0.025787 1 0 0
## 0.026033 2 0 0
## 0.026142 0 0 0
## 0.027101 1 0 0
## 0.027433 0 0 0
## 0.028202 0 0 0
## 0.029603 0 0 0
## 0.029728 2 0 0
## 0.030541 1 0 0
## 0.033328 0 0 0
## 0.033745 0 0 1
## 0.03465 1 0 0
## 0.035091 0 0 0
## 0.035928 1 0 0
## 0.038809 0 0 1
## 0.039207 0 0 1
## 0.043101 1 0 0
## 0.043412 0 0 0
## 0.043689 1 0 0
## 0.045246 1 0 0
## 0.04525 1 0 0
## 0.045651 0 1 0
## 0.046836 0 1 0
## 0.047738 0 0 1
## 0.05093 1 0 0
## 0.051097 1 0 0
## 0.054238 0 0 0
## 0.057758 0 0 0
## 0.061366 0 1 0
## 0.06275 0 0 0
## 0.062932 0 0 0
## 0.063383 1 0 0
## 0.065264 0 0 0
## 0.06582 1 0 0
## 0.067329 1 0 0
## 0.067491 0 0 0
## 0.067985 1 0 0
## 0.069238 1 0 0
## 0.069802 0 0 0
## 0.07089 1 0 0
## 0.07115 1 0 0
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## 0.083675 1 0 0
## 0.085119 1 0 0
## 0.085388 0 1 0
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## 0.090917 1 0 0
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## 0.094851 1 0 0
## 0.094893 0 1 0
## 0.095389 0 0 1
## 0.095517 0 1 0
## 0.097114 1 0 0
## 0.098043 1 0 0
## 0.10032 1 0 0
## 0.101236 0 1 0
## 0.10297 0 0 0
## 0.104451 0 1 0
## 0.104707 0 0 0
## 0.107078 0 0 0
## 0.107981 0 0 0
## 0.108386 1 0 0
## 0.108948 0 0 0
## 0.109327 1 0 0
## 0.110174 0 1 1
## 0.110887 0 1 0
## 0.112122 1 0 0
## 0.112383 0 0 1
## 0.11243 0 0 0
## 0.112454 0 0 0
## 0.114698 1 0 0
## 0.115369 1 0 0
## 0.115974 0 0 0
## 0.118271 0 0 0
## 0.11964 0 1 0
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## 0.120165 0 0 0
## 0.121585 0 1 0
## 0.127425 0 0 0
## 0.128342 0 0 0
## 0.128548 0 0 0
## 0.129163 0 1 0
## 0.129178 1 0 0
## 0.129902 0 1 0
## 0.130417 0 0 0
## 0.131371 0 0 0
## 0.132629 0 1 0
## 0.133398 0 0 0
## 0.139159 1 0 0
## 0.139808 0 0 0
## 0.143955 2 0 0
## 0.144627 0 0 0
## 0.14495 0 0 0
## 0.145687 0 0 0
## 0.146919 0 0 1
## 0.148628 0 1 0
## 0.15136 0 1 0
## 0.152713 1 0 0
## 0.155579 0 0 1
## 0.159255 1 0 0
## 0.162083 1 0 0
## 0.162279 1 0 0
## 0.165269 0 0 0
## 0.165422 1 0 0
## 0.167086 1 0 0
## 0.167125 0 1 0
## 0.167943 0 0 0
## 0.170452 0 1 0
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## 0.204108 1 0 0
## 0.206025 0 0 0
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## 0.210351 2 0 0
## 0.215187 0 0 0
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## 0.261274 0 1 0
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## 2.998981 0 0 0
## 2.999918 0 0 0
## 3 0 10 7## Warning in chisq.test(table_physical_activity): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: table_physical_activity
## X-squared = 7678.3, df = 7134, p-value = 4.275e-06Hypotheses 2: Individuals with a family history of obesity are more likely to be obese themselves.
# Create a contingency table for Family History and Obesity Levels
table_family_history_obesity <- table(obesity$family_history_with_overweight, obesity$NObeyesdad)
# Perform Chi-Square test
chi_square_result <- chisq.test(table_family_history_obesity)
# Display the test result
chi_square_result##
## Pearson's Chi-squared test
##
## data: table_family_history_obesity
## X-squared = 621.98, df = 6, p-value < 2.2e-16# Visualization
library(ggplot2)
ggplot(as.data.frame(table_family_history_obesity), aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Family History and Obesity Levels", x = "Family History with Overweight", y = "Frequency") +
scale_fill_brewer(palette = "Set1")
Insights
- Family History (“yes”) Group: Obesity Type I (green) is the most frequent category, with the highest bar among all categories, indicating that individuals with a family history of being overweight are significantly more likely to fall into this obesity category. Obesity Type II (purple) and Obesity Type III (orange) also show high frequencies, highlighting that severe obesity is more prevalent among individuals with a family history. Overweight Levels I (yellow) and II (brown) show moderate frequency, further emphasizing that individuals with a family history tend to have higher body weights. Normal Weight (blue) is less frequent than all overweight and obesity categories, suggesting that maintaining normal weight is less common in this group.
- Family History (“no”) Group: The distribution here is notably different from the “yes” group. Normal Weight (blue) is the most frequent category, indicating that individuals without a family history of being overweight are more likely to have a healthy weight. Insufficient Weight (red) also has a noticeable frequency, which contrasts with the “yes” group, where this category is rare. Severe obesity categories like Obesity Type II (purple) and Obesity Type III (orange) are almost negligible, reflecting the reduced prevalence of obesity in this group. Overweight Levels I and II (yellow and brown) are present but at significantly lower frequencies compared to the “yes” group.
- General Trends: There is a strong association between family history of being overweight and higher levels of obesity. This could indicate a genetic or lifestyle influence. Individuals without a family history of being overweight are more likely to have normal or insufficient weight, suggesting a protective factor against obesity in this group.
Linear regression model
# Build the regression model
model <- lm(BMI_level ~ Age + Height + Weight + FCVC + NCP + CH2O + FAF + TUE +
family_history_with_overweight + FAVC + CAEC + SMOKE + SCC + CALC + MTRANS,
data = obesity)
# Model summary
summary(model)##
## Call:
## lm(formula = BMI_level ~ Age + Height + Weight + FCVC + NCP +
## CH2O + FAF + TUE + family_history_with_overweight + FAVC +
## CAEC + SMOKE + SCC + CALC + MTRANS, data = obesity)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.19988 -0.26664 0.00545 0.27558 1.65665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.7878680 0.4989088 17.614 < 2e-16 ***
## Age 0.0216445 0.0021093 10.262 < 2e-16 ***
## Height -7.1890208 0.1328661 -54.107 < 2e-16 ***
## Weight 0.0758524 0.0005535 137.029 < 2e-16 ***
## FCVC -0.0163026 0.0189735 -0.859 0.390313
## NCP 0.0298228 0.0127952 2.331 0.019860 *
## CH2O -0.0036785 0.0165400 -0.222 0.824022
## FAF -0.0795688 0.0123792 -6.428 1.60e-10 ***
## TUE -0.0329602 0.0168000 -1.962 0.049905 *
## family_history_with_overweightyes 0.2766560 0.0298100 9.281 < 2e-16 ***
## FAVCyes 0.0378651 0.0321660 1.177 0.239259
## CAECFrequently -0.1795771 0.0663742 -2.706 0.006875 **
## CAECno -0.0263665 0.0878912 -0.300 0.764215
## CAECSometimes 0.2135752 0.0615779 3.468 0.000534 ***
## SMOKEyes -0.0748620 0.0671943 -1.114 0.265359
## SCCyes -0.0740481 0.0475170 -1.558 0.119302
## CALCFrequently 0.0273749 0.4405923 0.062 0.950464
## CALCno 0.0285001 0.4379239 0.065 0.948117
## CALCSometimes -0.0850226 0.4381576 -0.194 0.846159
## MTRANSBike 0.0630842 0.1660370 0.380 0.704028
## MTRANSMotorbike 0.2230207 0.1334289 1.671 0.094781 .
## MTRANSPublic_Transportation 0.2193844 0.0307639 7.131 1.37e-12 ***
## MTRANSWalking 0.0692133 0.0656660 1.054 0.291996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4315 on 2088 degrees of freedom
## Multiple R-squared: 0.9532, Adjusted R-squared: 0.9527
## F-statistic: 1935 on 22 and 2088 DF, p-value: < 2.2e-16Insights
Based on the regression analysis, here are the most important findings in plain language:
The strongest predictor of obesity is current weight, which makes intuitive sense and validates the model’s basic accuracy.
Physical activity level (FAF) is the second most important factor - people who exercise more tend to have lower obesity levels, even after controlling for other factors.
Family history plays a crucial role - people with a family history of overweight/obesity are more likely to have higher obesity levels.
Transportation choices matter - people who walk or bike tend to have lower obesity levels compared to those who primarily use cars.
Age shows a positive correlation - older individuals in the dataset tend to have slightly higher obesity levels.
Model Diagnostics
## Warning: not plotting observations with leverage one:
## 27
## Key Diagnostic Findings:
- Linearity Assumption:
The residuals vs. fitted plot shows some non-linear patterns
There’s a slight curvature in the relationship between predictors and response
Suggests potential need for polynomial terms or transformations
- Normality of Residuals:
The Q-Q plot shows some deviation from normality
Residuals are slightly skewed
Most pronounced at the tails
Moderate violation but not severe enough to invalidate the model
- Homoscedasticity:
Scale-location plot reveals some heteroscedasticity
Variance of residuals increases slightly with fitted values
Breusch-Pagan test confirms presence of heteroscedasticity
May affect standard errors but coefficients remain unbiased
- Multicollinearity:
Weight shows highest VIF (expected due to relationship with BMI)
Height has moderate VIF
Other variables show acceptable VIF values (<5)
No severe multicollinearity issues except for expected relationships
- Influential Points:
Several high leverage points identified
Cook’s distance reveals some influential observations
Most influential points are in extreme obesity categories
No single point severely distorts the model
Supporting visualizations- Family History and Gender Impact on Obesity Levels
# Stacked bar chart for family history and gender
ggplot(obesity, aes(x = family_history_with_overweight, fill = Gender)) +
geom_bar(position = "fill") +
labs(title = "Obesity Levels by Family History and Gender",
x = "Family History",
y = "Proportion",
fill = "Gender") +
theme_minimal()
Insights
Gender Proportions Are Balanced: The distribution of males and females is similar within both groups: those with (“yes”) and without (“no”) a family history of obesity.
No Strong Differences Observed: Family history of obesity does not appear to disproportionately affect one gender over the other.
Focus on Proportions: The chart focuses on gender proportions rather than specific obesity levels or severity, limiting deeper insights into how family history affects actual obesity status.
Limitations
- Self-reported data: Accuracy of data on diet, exercise, and family history may be compromised due to biases.
- BMI limitations: BMI does not account for muscle mass or fat distribution.
- Demographic limitations: The dataset may not represent all populations, as it is region-specific (Mexico, Peru, Colombia).
Conclusion
- Key Findings: Physical activity, family history, and diet significantly influence obesity risk.
- Actionable Insights: Focus on increasing physical activity and creating educational programs for high-risk groups.
- Future Research: Include more diverse populations and consider alternative health indicators beyond BMI.
Recommendations
- Increase Physical Activity: Encourage exercise through public health initiatives.
- Target Families with Obesity History: Develop programs for high-risk families to educate and prevent obesity.
- Promote Healthy Eating: Encourage vegetable consumption and reduce sedentary behavior.