Project Title : Cardiovascular Risk Prediction

Problem Description

The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD). The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes.

Data Description

• Sex: male or female("M" or "F")
• Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) 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.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history)
• BP Meds: whether or not the patient was on blood pressure medication (Nominal)
• Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal)
• Diabetes: whether or not the patient had diabetes (Nominal) Medical (current)
• Tot Chol: total cholesterol level (Continuous)
• Sys BP: systolic blood pressure (Continuous)
• Dia BP: diastolic blood pressure (Continuous)
• BMI: Body Mass Index (Continuous)
• Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.)
• Glucose: glucose level (Continuous) Predict variable (desired target)
• 10-year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”) - DV

Business Use Case:

• Early Risk Detection: By using cardiovascular risk prediction models, healthcare providers can identify patients who are at high risk of cardiovascular disease and take preventive measures.
• Personalized Treatment Planning: Predictive models can help healthcare providers tailor treatment plans based on a patient's individual risk profile, leading to more effective and personalized care.
• Improved Patient Outcomes: By detecting cardiovascular risk early and taking proactive measures, healthcare providers can reduce the incidence of heart disease and improve patient outcomes.
• Better Resource Allocation: Predictive models can help healthcare providers prioritize patients based on their risk level, leading to more efficient use of resources and improved patient care.
• Reduction in Healthcare Costs: By detecting and treating cardiovascular risk early, healthcare providers can reduce the cost of treatment and improve patient outcomes, leading to overall cost savings.
• Improved Public Health: By detecting cardiovascular risk early and implementing preventive measures, healthcare providers can improve public health and reduce the incidence of heart disease in the general population.

Data Pre-Processing

• Got the descriptive information of the data.
• checked for null values if any
• Checked for Outliers and duplicated values if any.

Data Cleaning

• Dropped the id column since it is of no significance to our model training
• Imputed null values with knn imputer and simple imputer
• treated outliers

Feature Engineering

• applied one hot encoding to is_smoking and sex columns and converted them to binary values
• separated continuos and discrete features
• checked corelation with heatmap and found sysBP and diaBp corelated
• created a new feature avg with the help of sysBP and diaBP

Exploratory Data Analysis

• Checked the target variable and fount it to e imbalanced. Risk to non risk rate ratio was 1:6 approx.
• checked for the distribution of independent variables with distplot and with the hue of target variable.
• Visualized age vs sex and found out the patients above age 50 are at high risk.
• Men are more likely to have risk related to heart.

Model training :

• Splitted data into indepdendent and dependent variable
• handled imbalanced target variable with the help of SMOTE
• Standardized resampled data using StandardScaler
• defined funvtions to train linear and non-linear models and get their performance metrics
• Performance metrics used were accuracy_score,precision_score, recall_score,f1_score
• trained models such as logistic regression, KNN, Decision trees,SVM,Random Forest and Found Random Forest as the best model with Recall of 93%.