A Step Towards Preventive Care: Leveraging Machine Learning for Heart Disease Risk Prediction

Predicting heart disease has been one of the most challenging jobs in the medical industry in recent decades. Heart disease claims the lives of about one person per minute in the modern era. Processing vast amounts of data in the healthcare industry requires the use of data science. Since predicting cardiac illness is a difficult undertaking, it is necessary to automate the process in order to minimize risks and notify patients well in advance. The Kaggle machine learning repository's dataset on heart disease is used in this paper. The suggested work uses logistic regression to categorize patients' risk level and forecast their likelihood of developing heart disease. As a result, this research provides a thorough analysis of the machine learning algorithm's performance in detecting the risk of a heart attack. The testing results confirm that, when compared to other ML algorithms used, the Logistic Regression approach has the greatest accuracy of 90.81%.