Heart-Disease-Prediction-using-ML

The project aims to identify the most suitable ML algorithm for the Heart Disease Prediction. For that, I selected SVM and Random Forest.

Introduction: This project aims to utilize machine learning algorithms to develop a predictive model for heart disease, potentially contributing to the early detection of heart disease, thereby reducing the global death rate of cardiovascular diseases.

Defining the ML problem:  Task (T): Build a heart disease risk prediction tool for the next 10 years using a machine model.  Experience (E): A dataset with details under different categories like demographic, behavioral, past, and current medical conditions will be used to train the model.  Performance Metric (P): To measure the performance of the model, metrics like Accuracy or Mean Absolute Error (MAE) will be used.

Data: Web link for the data set: https://www.kaggle.com/datasets/dileep070/heart-disease-prediction-usinglogistic-regression/data

Features - 14 features

1. Sex
2. Age
3. Current Smoker
4. Cigarettes Per Day (Cigs Per Day)
5. Blood Pressure Medication (BP Meds)
6. Previously had Stroke (Prevalent Stroke)
7. Hypertensive (Prevalent Hyp) No of examples: 4238 Possible methods of handling missing data  Imputation: Use the value of the median to fill in the blanks.

Algorithms:

1. SVM
2. Random Forest