Longitudinal study on noncommunicable diseases using machine learning

Title

Longitudinal study on noncommunicable diseases using machine learning

Creator

Deepak J.K.; Salma M.U.

Description

This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved.

Source

Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering, pp. 70-82.

Date

2024-01-01

Publisher

CRC Press

Coverage

Deepak J.K., Department of Statistics and Data Science, Christ (Deemed to Be University), Bengaluru, India; Salma M.U., Department of Statistics and Data Science, Christ (Deemed to Be University), Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-104014934-8; 978-103275923-4

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003476207-5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206550470&doi=10.1201%2f9781003476207-5&partnerID=40&md5=57bbfc8ab8ed680ad82d3efd6e19f1f4