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Heart Failure IV: Classification and Diagnostic Evaluation

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Updated: Oct 26, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison.

Md Mamun Ali¹, Bikash Kumar Paul², Kawsar Ahmed³

¹Department of Software Engineering (SWE), Daffodil International University (DIU), Sukrabad, Dhaka, 1207, Bangladesh.

Computers in Biology and Medicine

|July 27, 2021

Summary

This summary is machine-generated.

Machine learning accurately predicts heart disease. Random Forests achieved 100% accuracy, offering a valuable tool for early detection and clinical decision support in cardiology.

Keywords:

Cardiovascular disease Decision tree KNN Machine learning Random forest

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Cardiology
Computer Science
Data Mining

Background:

Cardiovascular diseases pose a significant global health challenge.
Inaccurate diagnoses and lack of expertise hinder effective heart disease management.
Digital patient records offer potential for improved diagnostic accuracy.

Purpose of the Study:

To identify the most accurate machine learning classifiers for early heart disease prediction.
To compare the performance of various supervised machine learning algorithms.
To rank features based on their importance for heart disease prediction.

Main Methods:

Applied and compared supervised machine learning algorithms: k-nearest neighbor (KNN), decision tree (DT), and random forests (RF).
Estimated feature importance scores for DT and RF algorithms.
Utilized a heart disease dataset from Kaggle for three-classification tasks.

Main Results:

Random Forests (RF) achieved 100% accuracy, sensitivity, and specificity.
Feature importance analysis identified key predictors for heart disease.
KNN and DT algorithms also showed promising predictive capabilities.

Conclusions:

Supervised machine learning, particularly RF, can predict heart disease with exceptional accuracy.
These models offer significant potential for clinical decision support and early diagnosis.
Simple machine learning algorithms can be highly effective for complex diagnostic tasks.