Paper Key : IRJ************162
Author: Pampari Naresh,Singidi Shreeya Reddy,Tenali Ebenezar,Ch Rajesh
Date Published: 04 Mar 2024
Abstract
Cardiovascular diseases (CVDs), including stroke and heart disease, remain leading causes of morbidity and mortality worldwide. Early identification of individuals at high risk of developing these conditions is crucial for preventive interventions and improving patient outcomes. In this study, we propose a machine learning-based approach for the prediction of stroke and heart disease risk.The dataset utilized comprises a comprehensive set of demographic, clinical, and lifestyle factors collected from a diverse population sample. Various machine learning algorithms, including Decision Trees, Support Vector Machines (SVM), and Random Forest (RF), are employed to develop predictive models. Among these algorithms, RF stands out as it combines the strength of Random Forest with an iterative process enhancing model performance to 90% accuracy and interpret-ability.This research contributes to advancing the field of cardiovascular risk assessment by leveraging machine learning techniques to develop accurate and interpretable predictive model. The proposed framework holds promise for enhancing early detection, risk stratification, and prevention of stroke and heart disease, ultimately leading to improved patient outcomes and reduced healthcare burden. Keywords: Cardiovascular disease, Stroke, Heart disease, Machine learning, Predictive modeling, Random Forest, Risk assessment, Healthcare.