Predictive Modeling for Heart Disease: An In-Depth Machine Learning Analysis

Predictive Modeling for Heart Disease: An In-Depth Machine Learning Analysis

Mamta Rani, Ashal Babu K

Department of Mathematics,

Chandigarh University, Mohali, India

Email: manudhamija20@yahoo.com

Abstract— Heart disease is a leading cause of mortality worldwide, and early detection is crucial for effective treatment and prevention. This research paper presents a comprehensive analysis of a machine learning model developed to predict the likelihood of heart disease based on various health and demographic factors. The study utilizes a real-world dataset and employs several machine learning algorithms to build and evaluate the predictive model. The paper discusses the data preprocessing steps, feature engineering techniques, and model evaluation metrics used. The results demonstrate the effectiveness of the proposed approach in accurately predicting heart disease, highlighting the potential of machine learning in healthcare applications.

Keywords— Heart Disease, Machine Learning, Random Forest, Data Preprocessing, Feature Engineering, Model Evaluation, Data Imbalance, Early Detection, Predictive Modeling