ANALYSIS OF CORONARY DISEASES USING MACHINE LEARNING MODELS (ACDMLM)

  • Unique Paper ID: 168296
  • Volume: 11
  • Issue: 5
  • PageNo: 572-577
  • Abstract:
  • The term "cardiovascular diseases" refers to a broad category of ailments that impact the heart. Approximately 20.5 million people die from these coronary diseases (CVDs) each year. In the last few decades, it has also been the leading cause of death globally. Right now, a precise and trustworthy method for obtaining an early disease diagnosis through task automation is required in order to carry out efficient management. To assist medical practitioners in diagnosing cardiac disease, numerous researchers employed a variety of data mining techniques. Nonetheless, fewer tests may be needed if data mining is used. A quick and efficient diagnostic method is essential to lowering the number of heart disease-related deaths. People can alter their lifestyles with the aid of early prognosis. If necessary, it also guarantees appropriate medical care. A quick and efficient diagnosis method is required to lower the number of heart disease-related deaths. By using several data mining approaches, including logistic regression, nearby K closest decision trees, and support vector machines, the suggested work forecasts the likelihood of cardiac illnesses. Consequently, a comparative analysis of the effectiveness of several machine learning algorithms is presented in this paper. This research describes a web-based system that uses basic parameters like smoking, diabetes, and cholesterol to estimate a person's risk of developing heart disease. A web-based approach to forecast the likelihood of developing coronary heart disease is established in this research. The test findings confirm that, in comparison to other applied machine learning methods, the Support Vector Machine reached a maximum accuracy of 86.76%.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 572-577

ANALYSIS OF CORONARY DISEASES USING MACHINE LEARNING MODELS (ACDMLM)

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