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Technology in the field of health care has been constantly enhanced incorporating machine learning and various other technologies for clinical decision support systems or computer-aided healthcare systems which assist in examining the complications. Heart Disease is an issue of concern for any age group and gender, which depends on innumerable factors like cholesterol, blood pressure, chest pain and more. Furthermore, initial methods of getting a check-up from the doctor to determine whether the patient is suffering or will be suffering from the disease is time consuming as well as laborious. Considering this, by using Machine Learning and Deep Learning, it is possible to overcome the complications and produce an effective and accurate means of detecting heart disease. This is achieved by deploying ANN, performing feature reduction by PCA as well and creating an ensemble model of Random Forest, Decision Tree, and KNN.

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Heart Disease Identification Using Ensemble Learning and Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Vahini Siruvoru,
  • Vijay Kumar Nampally,
  • Deepak Thati,
  • Sai Abhinav Devanaboyina,
  • Sai Dheeraj Reddy Sanagala

摘要

Technology in the field of health care has been constantly enhanced incorporating machine learning and various other technologies for clinical decision support systems or computer-aided healthcare systems which assist in examining the complications. Heart Disease is an issue of concern for any age group and gender, which depends on innumerable factors like cholesterol, blood pressure, chest pain and more. Furthermore, initial methods of getting a check-up from the doctor to determine whether the patient is suffering or will be suffering from the disease is time consuming as well as laborious. Considering this, by using Machine Learning and Deep Learning, it is possible to overcome the complications and produce an effective and accurate means of detecting heart disease. This is achieved by deploying ANN, performing feature reduction by PCA as well and creating an ensemble model of Random Forest, Decision Tree, and KNN.