Heart Disease Prediction Using Random Forest

Abstract

Heart disease remains a significant global health concern, and early prediction plays a pivotal role in effective prevention and management. This project leverages machine learning techniques to develop a heart disease prediction model using a comprehensive dataset encompassing critical patient attributes. The dataset includes features such as age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, smoking history etc. The primary objective is to create a robust predictive model capable of identifying individuals at risk of heart disease based on these input variables. Random Forest will be explored to evaluate its effectiveness in predicting heart disease outcomes. The Random Forest algorithm was selected due to its capability to handle complex, high-dimensional data and provide robust predictive performance. Our study involves preprocessing the data, handling missing values to ensure the quality and relevance of attributes. The dataset is then divided into training and testing sets to evaluate the model's performance