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The project goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD). The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts.

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Cardiovascular-Risk-Prediction

Introduction:

Predicting coronary heart disease in advance helps raise awareness for the disease. Preventive measurements like changing diet plans and exercise can slow down the progression of CHD. Early prediction can result in early diagnosis. So, we can treat the disease at an early stage and avoid more invasive treatment.

Problem Statement:

The goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD).

Data Description:

The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioural, and medical risk factors

Demographic:

Sex: male or female("M" or "F")

Age: Age of the patient

Behavioral:

is_smoking: whether or not the patient is a current smoker ("YES" or "NO")

Cigs Per Day: the number of cigarettes that the person smoked on average in one day

Medical(History):

BP Meds: whether or not the patient was on blood pressure medication

Prevalent Stroke: whether or not the patient had previously had a stroke

Prevalent Hyp: whether or not the patient was hypertensive

Diabetes: whether or not the patient had diabetes (Nominal) Medical(current)

Tot Chol: total cholesterol level

Sys BP: systolic blood pressure

Dia BP: diastolic blood pressure

BMI: Body Mass Index

Heart Rate: heart rate

Glucose: glucose level

CHD: 10-year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”)

Approach:-

Data preparation:

• Removed null values from the dataset

• Removed duplicate entries

• Used SMOTE and random under sampling to get a balanced dataset

Feature engineering:

• Extracted feature ‘Hypertension’ from systolic and diastolic blood pressure data

• Extracted feature ‘Diabetese severity’ from diabetes and glucose column

• Constructed new feature ‘Smoking Factor’ from number of cigarettes consumption

Conclusion:-

Using XGBoost and finetuning parameters with grid search and cross-validation I got a Recall of 92%.

About

The project goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD). The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts.

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