Title: Enhancing Diabetes Prevention and Prediction in India: Exploring Non-Invasive Monitoring Techniques and Integrative Approaches

Title: Enhancing Diabetes Prevention and Prediction in India: Exploring Non-Invasive Monitoring Techniques and Integrative Approaches

Aim

To investigate the potential of integrating non-invasive monitoring techniques with predictive diabetes calculation methods to enhance early detection and management in vulnerable populations in India.

Objectives

Conduct a comprehensive analysis of the prevalence and demographic determinants of diabetes in India, focusing on vulnerable population segments such as low-income groups, rural residents, and the elderly.

Identify key indicators of diabetes risk, including glucose levels and socio-economic factors, through an extensive review of existing literature and epidemiological data.

Evaluate a range of non-invasive monitoring techniques beyond traditional calculators, considering their suitability and effectiveness in diverse demographic cohorts within the Indian context.

Research Question

How can the integration of non-invasive monitoring techniques with predictive diabetes calculation methods be optimized to enhance early detection and management, particularly for vulnerable populations in India?

Description

This dissertation endeavours to provide a comprehensive analysis of the diabetes landscape in India, focusing on prevalence, trends, and demographic factors contributing to increased risk. Drawing upon existing literature, the research aims to identify populations vulnerable to diabetes, including those with low incomes, residing in rural areas, and advancing in age. By highlighting these factors, the dissertation will advocate for the prioritization of non-invasive solutions for diabetes prevention and prediction, tailored to specific demographic cohorts (Saravanan et al., 2018). The study will meticulously examine key indicators signalling an individual's susceptibility to diabetes, with a primary focus on glucose levels and other pertinent factors supported by scholarly research. Building upon this understanding, the research will emphasize the adoption of non-invasive methods for tracking these indicators within targeted demographic groups, emphasizing accessibility and user-friendliness (Vijayalaxmi et al., 2020).

Furthermore, the dissertation will explore a diverse array of non-invasive techniques beyond traditional calculators, considering socio-economic factors and demographic characteristics to justify the examination of various methods. This assessment will be crucial in determining the most suitable approaches for specific population segments, ensuring effective diabetes prevention and prediction strategies tailored to their unique needs. The concept of integration will be central to the research, delving into how individuals can take proactive steps towards managing their health, particularly in the context of diabetes. This evaluation will consider socio-economic backgrounds and demographic profiles to discern which non-invasive techniques are most conducive to fostering individual responsibility for health management. Ultimately, the dissertation will provide a comprehensive analysis of diabetes prevention and prediction strategies in India, advocating for the adoption of non-invasive monitoring techniques and integrative approaches tailored to diverse demographic cohorts (Laha et al., 2022).

References

Laha, S., Rajput, A., Laha, S.S. and Jadhav, R., 2022. A concise and systematic review on non-invasive glucose monitoring for potential diabetes management.Biosensors,12(11), p.965.

Saravanan, M. and Shubha, R., 2018. Non-invasive analytics based smart system for diabetes monitoring. InInternet of Things (IoT) Technologies for HealthCare: 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings 4(pp. 88-98). Springer International Publishing.

Vijayalaxmi, A., Sridevi, S., Sridhar, N. and Ambesange, S., 2020, May. Multi-disease prediction with artificial intelligence from core health parameters measured through non-invasive technique. In2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)(pp. 1252-1258). IEEE.