Predicting Heart Disease: A Machine Learning Approach to Early Detection

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Hasti D. Patel; Nirali Borad

Predicting Heart Disease: A Machine Learning Approach to Early Detection

Authors: Hasti D. Patel, Nirali Borad

Unique Paper ID: 176416
Volume: 11
Issue: 11
PageNo: 6069-6074

Keywords: Heart Disease Prediction Machine Learning (ML) Data Mining Early Detection Healthcare Technology.

Abstract:
Heart disease is a major global health issue, emphasizing the need for early detection. With advancements in biotechnology generating vast data, such as genetic and clinical records, machine learning (ML) has emerged as a promising tool for heart disease prediction. Identifying diseases based on symptoms alone is challenging and often reliant on doctors' expertise, which may not always yield accurate diagnoses. This research evaluates seven techniques: Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bays, and Support Vector Machine (SVM). Additionally, it proposes a data mining approach that leverages advanced techniques to facilitate early detection and prevention, offering significant benefits to both patients and doctors.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6069-6074

Predicting Heart Disease: A Machine Learning Approach to Early Detection

Available:https://ijirt.org/article?manuscript=176416

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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