Leveraging Machine Learning to Enhance Public Health Outcomes - A Comprehensive Approach to Disease Prediction, Prevention and Management

Aug 5, 2026

Upload by Levi

The integration of advanced machine learning (ML) techniques into public health systems offers transformative potential for improving disease prediction, prevention, and management. With the growth of large datasets and computing power, ML supports extracting insights and enabling data-driven decisions across healthcare. The paper reviews supervised learning, deep learning, and reinforcement learning for epidemiology, chronic disease management, healthcare accessibility, and health-outcome prediction, while addressing ethics, privacy, bias, cost reduction, improved patient care, and policy guidance through case studies and recent advancements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Overview of Public Health Challenges

Relevance of Machine Learning

Leveraging Machine Learning to Enhance Public Health Outcomes: A Comprehensive Approach to Disease Prediction, Prevention and Management SEEJPH Volume XXVI, 2025, ISSN: 2197-5248; Posted:04-01-25 Leveraging Machine Learning to Enhance Public Health Outcomes: A Comprehensive Approach to Disease Prediction, Prevention and Management Ashwini M. Save1, Tatwadarshi P. Nagarhalli2, Kirtida Naik3, Manish Rana4, Sunny Sall5, Anas Dange6, Shabina Sayed 7 1 Vidyavardhini's College of Engineering and Technology (VCET), Vasai, Mumbai, India 2 Vidyavardhini's College of Engineering and Technology(VCET), Vasai, Mumbai, India 3 Viva institute of Technology (VIT), Shrigaon, Virar, Mumbai, India 4St. John College of Engineering & Management (SJCEM) Palghar, Mumbai, India 5St. John College of Engineering & Management (SJCEM) Palghar, Mumbai, India 6Universal College of Engineering (UCE), Vasai Mumbai, India 7M.H. Saboo Siddik College Of Engineering (MHSSCE), Byculla, Mumbai-India KEYWORDS Machine Learning (ML), Disease Prediction, Chronic Disease Management, Healthcare Accessibility, Ethical Considerations etc. ABSTRACT: The integration of advanced machine learning (ML) techniques into public health systems offers transformative potential for improving disease prediction, prevention, and management. With the increasing availability of large datasets and computational power, ML has emerged as a powerful tool to extract insightsand make data-driven decisions in healthcare. This paper explores the application of various machine learning models, such as supervised learning, deep learning, and reinforcement learning, in addressing key challenges in public health. We discuss the impact of ML in areas such as epidemiology, chronic disease management, healthcare accessibility, and health outcomes prediction. Furthermore, we highlight the ethical considerations, data privacy concerns, and the potential for bias in ML systems when used in public health. This study also evaluates the effectiveness of novel ML techniques in reducing healthcare costs, improving patient care, and guiding public health policy development. Through case studies and a review of recent advancements, the paper presents recommendations for optimizing ML algorithms for more accurate, equitable, and efficient public health interventions. Introduction 1. Overview of Public Health Challenges Public health faces a multitude of global challenges, many of which are compounded by increasing populations, urbanization, and climate change. Pandemics, such as the recent COVID-19 crisis, highlight the vulnerabilities of health systems worldwide and underscore the need for rapid, data-driven responses. Chronic diseases, including diabetes, heart disease, and cancer, continue to burden health systems, leading to millions of preventable deaths each year. 1Dr. Ashwini M. Save: Associate Professor of Information Technology, Vidyavardhini’s College of Engineering and Technology, Vasai Road-401202, INDIA. E-Mail: [ashwini.save@gmail.com](ashwini.save@gmail.com). 2Dr. Tatwadarshi P. Nagarhalli: Associate Professor of Artificial Intelligence and Data Science , Vidyavardhini’s College of Engineering and Technology, Vasai Road-401202, INDIA. E-Mail: [tatwadarshipn@gmail.com](tatwadarshipn@gmail.com) 3Ms. Kirtida Naik: Assistant Professor of Computer Engineering, Viva Institute of Technology (VIT) Shirgaon, Virar-401305, [INDIA. E-Mail: kirtidanaik.28@gmail.com](INDIA. E-Mail: kirtidanaik.28@gmail.com) 4Dr. Manish Rana: Associate Professor of Information Technology, St. John College of Engineering & Management (SJCEM) Palghar-401404, INDIA. E-Mail: [dr.manish_rana@yahoo.co.in](dr.manish_rana@yahoo.co.in). 5Dr. Sunny Sall: Assistant Professor of Computer Engineering, St. John College of Engineering & Management (SJCEM) Palghar-401404, INDIA. E-Mail: [sunny_sall@yahoo.co.in](sunny_sall@yahoo.co.in). 6 Mr. Anas Dange: Assistant Professor of Artificila Intell

FAQs

How does machine learning improve disease prediction in public health?

Machine learning uses historical and real-time health data to identify patterns and forecast future events such as outbreaks and patient admissions.

Which machine learning approaches are discussed for public health applications?

The paper discusses supervised learning, deep learning, and reinforcement learning as key model families for addressing public health challenges.

What ethical and data-related concerns are highlighted when using ML in healthcare?

The document emphasizes ethical considerations, data privacy concerns, and the risk of bias in ML systems used for public health decisions.

Aug 5, 2026

Upload by Levi