STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microelectronics and Solid State Devices

Machine Learning Techniques for Early Detection of Heart Disease

  1. Anjali Pandey
  2. Anjali Tripathi
  3. Anamika Upadhyay
  4. Shalini Tripathi
  5. A. K. Dohare

Abstract

Cases of heart disease are increasing rapidly, thus it's important and concerning to be aware of any potential ailment beforehand. This diagnosis is a difficult task that must be completed fast and precisely. The primary goal of this study is to determine which patient, based on different medical features, has a higher chance of having heart disease. We created a heart disease prediction algorithm based on the patient's medical history to assess the possibility of receiving a heart disease diagnosis. To improve medical care, we employed the logistic regression method to predict and categorize heart disease patients using the provided heart disease prediction system. We have learned a great deal from this experiment that will aid in the prediction of heart disease patients. We have used machine learning and Python programming to forecast cardiac disease. We will learn about a wide range of heart-related illnesses from Heart Disease Prediction. As forecasting cardiac sickness is a challenging task, automation of the process is required to reduce risks and provide patients with plenty of warning. The primary goal of this project is to create a machine learning model that uses a variety of machine learning algorithms to forecast the likelihood of heart illness in the future and to identify key risk factors based on medical datasets that may contribute to heart disease. Predicting various diseases is a major difficulty in clinical data analysis. Large volumes of data produced by the healthcare industry can be effectively governed and decision-made with the help of machine learning.

Keywords

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