Improving Lifestyle Choices with Diabetes Prediction with Help of R Programming |
||
 |
 |
|
| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-7 |
||
| Year of Publication : 2024 | ||
| Authors : Saba Sultana, Vishal Patel | ||
| DOI : 10.14445/22312803/IJCTT-V72I7P108 | ||
How to Cite?
Saba Sultana, Vishal Patel, "Improving Lifestyle Choices with Diabetes Prediction with Help of R Programming," International Journal of Computer Trends and Technology, vol. 72, no. 7, pp. 69-73, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I7P108
Abstract
Diabetes prevalence is rising globally, demanding innovative preventative measures. This study explores the use of R programming for diabetes prediction to empower individuals with personalized lifestyle modifications. We develop a predictive model in R using a relevant dataset, identifying key risk factors through analysis. Based on the predicted risk, we propose targeted lifestyle changes for individuals, promoting preventative healthcare. This data-driven approach fosters informed decisionmaking, empowering individuals to adopt healthier habits and potentially mitigating diabetes development. This research contributes to the field of preventative healthcare by demonstrating the potential of R programming in tailoring lifestyle choices based on predicted diabetes risk.
Keywords
Diabetes, R programming, Lifestyle Modification, BMI, IDF.
Reference
[1] International Diabetes Federation (IDF), IDF Diabetes Atlas, Ninth Edition, 2021. [Online]. Available: https://diabetesatlas.org/
[2] World Health Organization, Diabetes. [Online]. Available: https://www.who.int/health-topics/diabetes
[3] American Diabetes Association, Understanding Type 2 Diabetes. [Online]. Available: https://diabetes.org/about-diabetes/type-2
[4] Centers for Disease Control and Prevention, Preventing Type 2 Diabetes, 2024. [Online]. Available: https://www.cdc.gov/diabetes/prevent-type-2/index.html
[5] American Diabetes Association Professional Practice Committee, “Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2022,” Diabetes Care, vol. 45, pp. S17–S38, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Gregor Stiglic, Roger Watson, and Leona Cilar, “R You Ready? Using the R Programme for Statistical Analysis and Graphics,” Research in Nursing & Health, vol. 42, no. 6, pp. 494-499, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hadley Wickham, Ggplot2: Elegant Graphics for Data Analysis, Springer International Publishing, pp. 1-260, 2016.
[Google Scholar] [Publisher Link]
[8] World Health Organization, Global Report on Diabetes, 2016. [Online]. Available: https://www.who.int/publications/i/item/9789241565257
[9] Narges Razavian et al., “Population-Level Prediction of Type 2 Diabetes from Claims Data and Analysis of Risk Factors,” Big Data, vol. 3, no. 4, pp. 277-287, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] American Diabetes Association, “Lifestyle Management: Standards of Medical Care in Diabetes—2021,” Diabetes Care, vol. 44, Supplement 1, pp. S81-S92, 2021. Not found
[11] Paul W. Franks, and Alaitz Poveda, “Lifestyle and Precision Diabetes Medicine: Will Genomics Help Optimise the Prediction, Prevention and Treatment of Type 2 Diabetes through Lifestyle Therapy?,” Diabetologia, vol. 60, no. 5, pp. 784–792, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] George Shaw Jr, Tarun Sharma, and Sthanu Ramakrishnan, “Exploring Diabetes and Users’ Lifestyle Choices in Twitter to Improve Health Outcomes,” Proceedings of the Southern Association for Information Systems Conference, Simons Island, Georgia, USA, pp. 1- 6, 2019.
[Google Scholar]
[13] Nice Maylani Asril et al., “Predicting Healthy Lifestyle Behaviours among Patients with Type 2 Diabetes in Rural Bali, Indonesia,” Clinical Medicine Insights: Endocrinology and Diabetes, vol. 13, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Wenwen Du et al., “Thirty-Year Urbanization Trajectories and Obesity in Modernizing China,” International Journal of Environmental Research and Public Health, vol. 19, no. 4, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Asish Satpathy, and Satyajit Behari, “Machine Learning Prediction of Chronic Diabetes Based on a Person's Demography and Lifestyle Information,” International Journal of Data Science, vol. 7, no. 3, pp. 210-228, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Surabhi Kaul, and Yogesh Kumar, “Artificial Intelligence-based Learning Techniques for Diabetes Prediction: Challenges and Systematic Review,” SN Computer Science, vol. 1, no. 6, pp. 1-7, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Quan Zou et al., “Predicting Diabetes Mellitus with Machine Learning Techniques,” Frontiers in Genetics, vol. 9, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Carla K. Miller et al., “Development and Pilot Testing of a Novel Behavioral Intervention for Adults with Type 2 Diabetes Using Intervention Mapping,” Health Psychology and Behavioral Medicine, vol. 5, no. 1, pp. 317-336, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] George Shaw Jr, Tarun Sharma, and Sthanu Ramakrishnan, “Exploring Diabetes and Users' Lifestyle Choices in Digital Spaces to Improve Health Outcomes,” SAIS 2019 Proceedings, pp. 1-7, 2019.
[Google Scholar] [Publisher Link]
[20] Irene Dankwa-Mullan et al., “Transforming Diabetes Care through Artificial Intelligence: The Future is Here,” Population Health Management, vol. 22, no. 3, pp. 229-242, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Aishwariya Dutta et al., “Early Prediction of Diabetes Using an Ensemble of Machine Learning Models,” International Journal of Environmental Research and Public Health, vol. 19, no. 19, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Kevin Plis et al., “A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management,” Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 35-39, 2014.
[Google Scholar] [Publisher Link]
[23] Ann Smith Barnes, “The Epidemic of Obesity and Diabetes,” The Texas Heart Institute Journal, vol. 38, no. 2, pp. 142-144, 2011.
[Google Scholar] [Publisher Link]
[24] American Diabetes Association, What Is the A1C Test?. [Online]. Available: https://diabetes.org/about-diabetes/a1c