Heart Disease Prediction Using Artificial Intelligence And Machine Learning

Heart Disease Prediction Using Artificial Intelligence and Machine Learning

Kiran Pustode1, Rahul Navkhare2

1Student of M-Tech Artificial Intelligence & Data Science Engineering Department in WCEM, Nagpur

2Professor of M-Tech Artificial Intelligence & Data Science Engineering Department in WCEM, Nagpur

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Abstract - Heart disease is one of the most significant causes of global mortality since its intricacy and the rate of misdiagnosis have brought a great challenge to medical workers. As machine learning has shown robust efficacy in decision-making and predictions, it is essential to construct a machine learning model to assist with heart disease diagnosis. The cost of healthcare is outpacing both corporate and governmental budgets as a result of silent illnesses like cardiovascular disorders. Early diagnosis and treatment of these illnesses are therefore desperately needed. One of the newest and most popular technologies is machine learning, which is being used to anticipate diseases in a variety of industries worldwide, including healthcare. The purpose of this study is to use logistic regression to forecast the overall risks and find the most important heart disease predictors. In order to find the predictors, the binary logistic model, one of the classification techniques in machine learning, is employed in this work.

Key Words: Machine learning, Logistic regression, classification algorithms, heart diseases