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October 18, 2023

The study developed a machine learning model using Gradient Boosting Classifier with hyperparameter adjustment and 5-fold cross-validation to predict heart disease, though specific accuracy metrics are not reported in the abstract.

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Why the study?

Predicting heart disease is difficult in medicine, and improving model correctness remains a major challenge.

Design

Machine learning model development and validation study

Authors

MPM. Jahir PashaJain UniversityKAKiraniwale Aejaz AhmedSAShaikh Mohammed Amair

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Overview

Requires prospective validation before clinical use; extends ML applications in cardiology but remains hypothesis-generating.

Structured PICO

P
Population
Patients with heart disease (specific details not provided in the abstract)
I
Intervention
Machine learning models (Gradient Boosting Classifier with hyperparameter adjustment) and IoT integration
O
Outcome
Prediction of heart syndrome/model accuracy

This study proposes a machine learning and IoT-based approach using a Gradient Boosting Classifier to predict the occurrence of heart disease.

Cite This Study

Pasha et al. (2023) studied this question.

synapsesocial.com/papers/6a19a54e3e4c9aaeb7f6500fhttps://doi.org/10.1109/icssas57918.2023.10331823
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Performance Comparison of Machine Learning Algorithms in Heart Disease Prediction with Enhanced Accuracy through Hyper parameter Tuning2025
  2. 2Heart disease diagnosis and prediction based on hybrid machine learning model2022
  3. 3A Comprehensive Analysis of Five Machine Learning Models for Predicting Heart Disease2026
  4. 4Heart Disease Prediction: A Comparative Analysis of Machine Learning Algorithms2024
  5. 5Prediction of Heart Disease by using Machine Learning Algortihms2024