Early Prediction of Heart Disease

Registration ID: IJNRD_212604

Published ID: IJNRD2401174

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Sauleh Shabir, Sufiyan Ahmed Mujawar, Sagarika Das, K Susheel Kumar

Early Heart Disease Prediction, Machine Learning, , KNN, SVC, Logistic Regression, Decision Trees, Gradient Boosting, Random Forest, Tkinter (GUI).

In the realm of healthcare and medical research, early disease prediction stands as a pivotal pillar for improving patient prognosis and curbing healthcare expenses. This study delves into the application of various machine learning algorithms like as Random Forest, Logistic Regression, Support Vector Machines (SVC), Decision Trees, and Gradient Boosting—for the accurate prediction of heart diseases. The primary goal is to develop robust predictive models that effectively analyze medical data, and contribute to timely identification and precise prognosis of cardiovascular conditions. This report synthesizes findings from an extensive literature review that scrutinizes multiple studies related to and around heart disease prediction using diverse machine learning methodologies. The analysis encompasses distinct approaches and datasets, showcasing the performance of algorithms in predicting heart diseases based on varying parameters and attributes. The comprehensive comparative analysis and evaluation conducted in this report aim to determine the superior-performing algorithms for heart disease prediction, contributing significantly to enhanced diagnostic precision and better patient care. This synthesis of various studies underscores the pivotal role of machine learning in revolutionizing healthcare, providing a roadmap for optimized models and potential real-world applications in heart disease diagnosis and prognosis.

Published Paper   E-Certificate

Sauleh Shabir, Sufiyan Ahmed Mujawar, Sagarika Das, & K Susheel Kumar (January-2024). Early Prediction of Heart Disease. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(1), b643-b645. https://ijnrd.org/papers/IJNRD2401174.pdf

Volume 9 Issue 1, January-2024

Pages : b643-b645

Paper Reg. ID: IJNRD_212604

Published Paper Id: IJNRD2401174

Downloads: 000122255

Research Area: Science & Technology

Author Type: Indian Author

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2401174.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2401174

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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