Artificial Intelligence Powered Early Detection of Heart Disease

Abstract

In the healthcare industry, Machine Learning (ML) plays a crucial role in disease prediction. A patient must go through a series of tests before a condition can be diagnosed. However, using machine learning techniques, the number of tests can be reduced. This simplified test has a significant impact on both time and performance. Early patient care has benefited from sound medical data analysis due to the growing amount of data generated by the medical and healthcare sectors. With the help of disease data, massive amounts of medical data can be mined for hidden pattern information. With a focus on heart diseases, this study evaluates and suggests a heart disease prediction based on the patient's symptoms using machine learning techniques such as SVM, MLR, and RF algorithms. The proposed method outperforms those currently in use in terms of accuracy, forecast speed, and consistency of outcomes. It is also appropriate to classify lung cancers using trained datasets for accurate identification.