Predictive Modeling for Hypertension Detection: A Machine Learning Approach

Registration ID: IJNRD_222012

Published ID: IJNRD2405537

DOI: Click Here to Get

ANIRBAN MONDAL , Kankana Hazra

Hypertension, Random Forest, Logistic Regression, Support Vector Machine, Decision Tree.

Millions of people worldwide suffer from hypertension, often known as high blood pressure, which can have serious consequences for their cardiovascular system if left undiagnosed and untreated. Effective preventative measures and therapy for hypertension depend on early and precise recognition of the condition. This project aims to create a machine learning-based system for detecting hypertension, offering a scalable and affordable means of quickly identifying those at risk. The suggested system uses cutting-edge machine learning algorithms, such as Random Forest, Logistic Regression, Support Vector Machine, and Decision Tree methods, to evaluate pertinent medical data and make precise predictions about the state of hypertension. The main input data sources are physiological indicators like blood pressure readings, heart-rate, and demographic data. The model also considers medical history and lifestyle factors, providing a thorough method for detecting hypertension.

Published Paper   E-Certificate

ANIRBAN MONDAL & Kankana Hazra (May-2024). Predictive Modeling for Hypertension Detection: A Machine Learning Approach. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), f351-f359. https://ijnrd.org/papers/IJNRD2405537.pdf

Volume 9 Issue 5, May-2024

Pages : f351-f359

Paper Reg. ID: IJNRD_222012

Published Paper Id: IJNRD2405537

Downloads: 000121980

Research Area: Engineering

Country: Birbhum, West Bengal, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405537.pdf

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

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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