The document discusses using a logistic regression machine learning model to predict the risk of coronary heart disease (CHD) and its associated risk factors, emphasizing the importance of preventive measures in healthcare. It highlights the dataset used, which contains over 4200 observations and 14 independent variables, and reports an accuracy of 85.7% when evaluating the model. Additionally, the model can assist hospitals in identifying at-risk patients and optimizing preventive care strategies to reduce healthcare costs.

1. Data Science for
Health Care
Use Case:
Heart Risk Analytics
in Python
Chetan Khanzode

2. WHO Report
• Coronary heart disease (CHD) and stroke are the
leading causes of death in both economically
developed and developing countries. Each year, more
than 17 million people die from cardiovascular
disease worldwide.
• It is number 1 cause of death globally

3. Health Domain- CHD – ML Model
• CHD is a disease of the blood vessels
supplying the heart.
• Logistic regression ML model can be used
to predict risk of CHD
• Collection of Health and behavioural data,
improve overall health of individuals by
identifying preventive measure to reduce
cost with best treatment to individual.

4. Health Domain- CHD – Risk Factors
Risk factors are variables that increase the chances of a
disease
• Demographic risk factors
– Gender: sex of patient
– Age: Age in years
– Education: education of patient
• First medical examination Risk factors
– TotChol: Total cholesterol (mg/dL)
– SysBP: Systolic blood pressure
– DiaBP: Diastolic blood pressure
– BMI: Body Mass Index, weight (kg)/height (m)2
– HeartRate: Heart rate (beats/minute)
– Glucose: Blood glucose level (mg/dL)

5. Build Predictive Model
Data Input
Impute the
Missing
Values with
median
values
Randomly split
the dataset into
Training and Test
sets
Train the Logistic
regression Model on
training set to predict
whether or not patient
experience CHD within
10 years of first medical
examination
Evaluate predictive
model on test set for
future set of
observations

6. Health Data Input – Dataset
Dataset used contains 4200+ observations
14 independent variables
1 dependent variable

7. Import Relevant Libs and Impute
missing values

8. Quick insight into data

9. Quick insight into data

10. Quick insight into data - Grouping

11. Quick insight into data - correlation

12. Quick insight into data - SysBP

13. Quick insight into data - Heatmap

14. Train and Test set , CM metric
The model accuracy on test data is 85.7%.

15. Confusion Matrix

16. Model Usage
• “Prevention is better than cure”. The model can help to predict CHD risk to individual and
they can adapt to healthy style and avoid expenses in later stage of life.
• Hospitals can run the health campaign with this predicted model to identify set of patients
who are at risk of CHD. Also it can be used for new set for medical examinations.
• $Revenue = x*$y*$z – $o
Where
– x is number of people who enrol for preventive care
– $y is amount of expenses
– Period of say z( 5 to 10 years).
– Overhead (for say marketing campaign etc.)
– Also as next step can be to reduce overhead by implementing AI solutions so as to
improve the revenue.

17. Thank you