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Machine Learning Boosts Heart Disease Prediction Accuracy

Sami Ullah, Muhammad Mohsin Khan· August 20, 2026 View original

Key takeaways

  • Machine learning models can significantly improve the accuracy of heart disease prediction.
  • SVM and Simple Cart models showed superior performance on different datasets.
  • Early and accurate detection is crucial for improving patient outcomes.
  • ML offers critical support for clinical decision-making in cardiology.

Who benefits

HealthcarePharmaceuticalsHealthTechInsurance

Summary

This research compares various machine learning classifiers for heart disease prediction using two public datasets. It finds that Support Vector Machine (SVM) and Simple Cart models achieve the highest accuracy and lowest error rates, demonstrating ML's potential to significantly aid early diagnosis.

Heart disease remains a leading cause of global mortality, underscoring the critical need for early and precise detection to enhance patient outcomes. This study delves into the predictive analysis of heart disease by employing various machine learning (ML) techniques. Its primary goal was to pinpoint the most accurate and least error-prone method for diagnosis by comparing the performance of multiple classifiers.The researchers utilized two publicly available datasets from UCI and Kaggle, each containing 14 attributes relevant to heart health indicators. A range of ML techniques were applied, including J48, Naive Bayes, Logistic Regression, Simple Cart, Bagging, Decision Stump, AdaBoost, Artificial Neural Networks, and Support Vector Machine (SVM). The performance of these models was rigorously evaluated using standard metrics such as Mean Absolute Error (MAE), Relative Absolute Error (RAE), accuracy, precision, recall, and F-measure.The results indicated that SVM delivered the highest performance on the UCI dataset, while Simple Cart proved most effective on the Kaggle dataset, both achieving superior accuracy and minimal error rates. The study concludes that well-tuned and validated ML models can substantially contribute to the early diagnosis of heart disease, offering crucial support for clinical decision-making. Future work is suggested to explore hybrid approaches and incorporate more recent datasets to further refine prediction accuracy.

Why it matters

Healthcare professionals and medical technology developers can leverage these findings to implement more accurate and efficient diagnostic tools, leading to earlier interventions and improved patient care for heart disease.

How to implement this in your domain

  1. 1Integrate top-performing ML models like SVM or Simple Cart into clinical decision support systems for heart disease.
  2. 2Validate these ML models with proprietary patient data to ensure local applicability and performance.
  3. 3Collaborate with ML experts to fine-tune models and explore hybrid approaches for enhanced accuracy.
  4. 4Develop user-friendly interfaces for clinicians to interpret ML-driven predictions effectively.

Original post by Sami Ullah, Muhammad Mohsin Khan

"arXiv:2608.18687v1 Announce Type: new Abstract: Heart disease remains the leading cause of mortality globally, necessitating early and accurate detection to improve patient outcomes. This research focuses on the predictive analysis of heart disease using machine learning (ML) tec…"

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