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The heart plays a crucial role in the survival of living organisms by serving as the central pump that circulates blood throughout the body. Heart disease is often considered one of the most life-threatening conditions in humans due to its significant impact on health and mortality rates. The healthcare sector accumulates massive volumes of healthcare data, but regrettably, much of this valuable information often remains untapped, preventing the industry from uncovering hidden insights that could enhance decision-making processes. Hidden patterns and relationships are often left undiscovered and underutilized. The use of advanced machine learning techniques offers a promising solution to address the complexities involved in predicting heart diseases. It represents a crucial challenge within the realm of clinical data analysis, as accurately identifying and forecasting heart-related conditions is of paramount importance for improving healthcare outcomes. Early prediction of heart attack may save many lives hence preventing these has become more than necessary. Machine learning (ML) has the potential to provide highly efficient solutions for decision- making and precise predictive capabilities. In the field of machine learning, there are several classification models such as Logistic Regression, K- nearest neighbors (K-NN), and Support Vector Machine (SVM) that have proven effective in achieving the goal of predicting and diagnosing heart diseases. Decision Tree Classifier, in particular, serves as a valuable decision support system for detecting and forecasting heart diseases and heart attacks in individuals by utilizing risk factors associated with heart disease. Datasets containing medical parameters play a pivotal role in this process, as they are processed through various machine learning algorithms. These algorithms help uncover correlations among the different attributes present in the dataset using standard machine learning techniques. The overarching aim of this project is to employ machine learning techniques for the prediction and diagnosis of heart diseases, contributing to better healthcare outcomes.

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An Effective Prediction of Heart Diseases Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • O. Obulesu,
  • Koulampeta Yakshitha,
  • Sanjana Vuyyuru,
  • Tumkur Meenakshi,
  • Pujala Vaishnavi

摘要

The heart plays a crucial role in the survival of living organisms by serving as the central pump that circulates blood throughout the body. Heart disease is often considered one of the most life-threatening conditions in humans due to its significant impact on health and mortality rates. The healthcare sector accumulates massive volumes of healthcare data, but regrettably, much of this valuable information often remains untapped, preventing the industry from uncovering hidden insights that could enhance decision-making processes. Hidden patterns and relationships are often left undiscovered and underutilized. The use of advanced machine learning techniques offers a promising solution to address the complexities involved in predicting heart diseases. It represents a crucial challenge within the realm of clinical data analysis, as accurately identifying and forecasting heart-related conditions is of paramount importance for improving healthcare outcomes. Early prediction of heart attack may save many lives hence preventing these has become more than necessary. Machine learning (ML) has the potential to provide highly efficient solutions for decision- making and precise predictive capabilities. In the field of machine learning, there are several classification models such as Logistic Regression, K- nearest neighbors (K-NN), and Support Vector Machine (SVM) that have proven effective in achieving the goal of predicting and diagnosing heart diseases. Decision Tree Classifier, in particular, serves as a valuable decision support system for detecting and forecasting heart diseases and heart attacks in individuals by utilizing risk factors associated with heart disease. Datasets containing medical parameters play a pivotal role in this process, as they are processed through various machine learning algorithms. These algorithms help uncover correlations among the different attributes present in the dataset using standard machine learning techniques. The overarching aim of this project is to employ machine learning techniques for the prediction and diagnosis of heart diseases, contributing to better healthcare outcomes.