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Heart Disease Prediction using Machine Learning Techniques

Year: 2024

Type: article

Abstract: Throughout their growth, artificial intelligence and machine learning have shown beneficial in multiple areas, particularly with the huge amount of data that has been generated recently. Making quicker and more accurate decisions regarding illness forecasts may be more dependable. The model can be used in the visualization and analysis of diseases. The article compares between different machine learning algorithms. In addition to numerous machine learning techniques, the UCI dataset is employed. Testing was done on Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest and Decision Tree algorithms. After performing the mentioned algorithm, it indicates that the decision tree performs better than another algorithm, its an accuracy of 98.54%.

Source: Multicriteria Algorithms with Applications

DOI: 10.61356/j.mawa.2024.5430

Authors: Raghad Ahmed Gad, Salma Saad Abdelshakour, Ahmed A. Eldouh

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4404659040

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