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Early heart disease prediction using feature engineering and machine learning algorithms.

Mohammed Amine Bouqentar1, Oumaima Terrada1, Soufiane Hamida1,2,3

  • 1EEIS Laboratory, ENSET of Mohammedia, Hassan II University of Casablanca, Mohammedia, Morocco.

Heliyon
|October 14, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms can significantly improve early heart disease prediction and diagnosis. This study compared multiple ML models using heart datasets, identifying optimal algorithms for enhanced cardiovascular disease detection.

Keywords:
Artificial intelligenceCardiovascular diseasesClassificationDeep learningMachine learningPrediction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Research

Background:

  • Cardiovascular diseases (CVDs) are a leading cause of global mortality, responsible for 32% of worldwide deaths.
  • Accurate and timely diagnosis of heart disease is crucial for effective patient management and treatment.
  • Despite advancements, misdiagnosis and misinterpretation of results remain challenges for healthcare professionals.

Purpose of the Study:

  • To develop a machine learning (ML) system for the early prediction of cardiovascular diseases.
  • To conduct a comparative analysis of various ML algorithms to identify the most effective ones for CVD prediction.
  • To enhance the accuracy and reliability of heart disease diagnosis through advanced computational methods.

Main Methods:

  • Utilized the Cleveland and Statlog heart datasets for training and validation of ML models.
  • Trained and evaluated multiple ML algorithms including Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Adaptive Boosting, and K-Nearest Neighbors.
  • Assessed algorithm performance using metrics like accuracy, precision, recall, F1 score, and Area Under the Curve, with hyperparameter tuning and 10-fold cross-validation.

Main Results:

  • The comparative analysis demonstrated the significant potential of ML algorithms in improving the early prediction and diagnosis of cardiovascular diseases.
  • Specific ML algorithms showed superior performance in identifying CVD risk factors and predicting disease onset.
  • The developed ML system provides a robust tool for healthcare professionals, validated on established heart datasets.

Conclusions:

  • Machine learning offers a powerful approach to augment the diagnostic capabilities of healthcare professionals in cardiology.
  • The study validates the effectiveness of selected ML algorithms for early CVD detection, contributing to advancements in medical AI.
  • The developed ML system has the potential to reduce misdiagnosis rates and improve patient outcomes for cardiovascular diseases and potentially other conditions.