Prediction of Heart Diseases using Machine Le...

Prediction of Heart Diseases using Machine Learning

Author Name : Mr. Shaik. Sikindar, G. Samba Siva Rao, Abhishek Anand, M.Vrajachand

ABSTRACT

Cardiovascular disease is the leading cause of death among all diseases. The number of men and women diagnosed with heart disease is increasing each year.  Heart disease is  a major global health problem, and effective treatment  and prevention depend heavily on early detection. Machine learning algorithms  based on patient data have shown promise in predicting and diagnosing heart disease. This overview provides an overview of key factors and approaches in applying machine learning to predict heart disease. The proposed method involves the collection of a wide range of patient data, including demographics, medical history, electrocardiogram (ECG) readings, blood pressure, cholesterol levels, and numerous laboratory tests including other measurements. Machine learning models are trained and evaluated based on this data. The proposed approach uses a random forest to predict whether heart disease will occur. After training, the predictive model  can be  used to predict the probability of heart disease in new, yet unseen patient data. A predictive model is trained using a dataset containing historical patient data. These predictions help doctors make early diagnoses, assess risks, and adjust treatment. Using machine learning to predict heart disease has many advantages. This will enable the creation of accurate and effective decision support systems that help healthcare professionals identify high- risk patients and take early action. This preventive strategy saves lives and reduces strain on the health system. RF can accurately identify whether a person suffers from heart disease based on various clinical and demographic variables. Integrating machine learning into healthcare systems could improve patient outcomes, use healthcare resources more effectively, and revolutionize the management of heart disease.

Key Words: cardiovascular disease, Diagnosed, Machine learning, Electrocardiogram, RF