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Updated: Oct 2, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models.

K S Archana¹, B Sivakumar², Ramya Kuppusamy³

¹Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.

Computational and Mathematical Methods in Medicine

|February 25, 2022

Summary

This summary is machine-generated.

Machine learning models accurately predict cardiovascular disease (CHD) risk using patient data. This approach improves early detection and supports healthcare professionals in competent patient analysis.

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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

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In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Cardiovascular disease (CHD) is a leading global cause of death, necessitating accurate early prediction for effective treatment.
Traditional prediction methods struggle with complex data and relationships, highlighting the need for advanced analytical techniques.

Purpose of the Study:

To apply machine learning (ML) to predict heart disease risk from historical medical data.
To uncover data correlations for improved prediction accuracy using various ML models.

Main Methods:

Implementation of Naive Bayes and Random Forest algorithms, including hybrid approaches.
Utilizing 14 key patient parameters such as age, sex, blood sugar, and chest discomfort for analysis.

Main Results:

Achieved a high prediction accuracy of 93% for identifying heart disease.
The system provides probability percentages for developing heart disease.

Conclusions:

The proposed ML system effectively identifies heart disease, offering improved prediction accuracy.
This method aids physicians in competently analyzing heart patients and potentially reducing mortality rates.