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Related Experiment Video

Updated: Oct 26, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.

Rohit Bharti1, Aditya Khamparia2, Mohammad Shabaz3

  • 1School of Computer Science and Engineering, Lovely Professional University, Phagwara, India.

Computational Intelligence and Neuroscience
|July 26, 2021
PubMed
Summary

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Accurate heart disease prediction is vital. This study applied machine learning and deep learning to the UCI Heart Disease dataset, achieving 94.2% accuracy with deep learning for improved patient outcomes.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Accurate heart disease prediction is crucial for preventing fatal outcomes.
  • The UCI Machine Learning Heart Disease dataset was utilized for analysis.
  • Existing methods require improvement for enhanced diagnostic accuracy.

Purpose of the Study:

  • To compare the performance of various machine learning and deep learning algorithms for heart disease prediction.
  • To identify and handle irrelevant features within the dataset.
  • To explore the integration of predictive models with multimedia technologies like mobile devices.

Main Methods:

  • Application of multiple machine learning algorithms and deep learning techniques.
  • Feature selection and noise reduction using Isolation Forest.

Related Experiment Videos

Last Updated: Oct 26, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
  • Data normalization to optimize model performance.
  • Validation of results using accuracy metrics and confusion matrix analysis.

    • Main Results:

    • Promising results were achieved across different algorithms.
    • Deep learning models demonstrated superior performance, reaching 94.2% accuracy.
    • Irrelevant features were effectively handled, and data normalization improved outcomes.

    Conclusions:

    • Machine learning and deep learning approaches show significant potential in accurate heart disease prediction.
    • Deep learning offers a highly accurate method for analyzing heart disease data.
    • Future integration with mobile technology could enhance accessibility and real-time monitoring.