Heart Disease Prediction

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Sahil Yadav; Varun Sakharkar; Arham Sancheti; Sagar Rghatvan

Heart Disease Prediction

Authors: Sahil Yadav, Varun Sakharkar, Arham Sancheti, Sagar Rghatvan

Unique Paper ID: 176460
Volume: 11
Issue: 11
PageNo: 8052-8057

Keywords: Data Analysis Heart Disease Logistic Regression Machine Learning Predictive Health Python Scikit-learn Accuracy Metrics Early Detection Healthcare Model Training Predictive Model Data Preprocessing User Interface User-Friendly Design.

Abstract:
This project takes advantage of machine learning techniques to predict the possibility of heart disease in individuals based on various health characteristics, from the title "predicting heart disease". A major cause of mortality due to heart diseases is, the purpose of this initiative is to increase early identity and preventive care through data-operated insights. The platform processes a dataset with significant health parameters such as age, penis, cholesterol levels, blood pressure, and exercise habits, used to train a logistic region model. This model classifies individuals into two groups: who are at risk of heart disease and which are not. The prediction system is designed to be comfortable, enabling users to input their health information and receive an accurate diagnosis. By incorporating the use of python, the panda library for data manipulation, and scikit-learn for model implementation, provides a broad, reliable solution for project heart disease prediction. In addition, the model is adapted to the user access, offering a simple interface and rapid response time. It discusses the design, implementation and testing stages of the paper system, highlighting the model's accuracy, performance matrix and potential applications in real -world healthcare scenarios. Through this platform, we aim to contribute to the growing field of future health technologies and support early intervention in cardiovascular care.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 8052-8057

Heart Disease Prediction

Available:https://ijirt.org/article?manuscript=176460

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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