ABSTRACT
It is vital to estimate and predict the chronological risk rate of individuals of diabetes mellitus and its complications through non-invasive or minimally invasive methods. Data mining and machine learning techniques are applied to health data repositories to achieve this goal. Although past studies have used various combinations of technologies for the assessment and prediction of diabetes and its complications, there is a lack of attention to combining temporal data with a visual representation assessment technique, which can be widely accepted. Further, prediction of risk throughout the lifetime of an individual in a chronological manner by considering their future changes with respect to the characteristics of a similar cohort is something worth contemplating for accurate risk prediction models. We aim to introduce a simple, powerful visualization technique to self-monitoring, which will be highly beneficial in enhancing the health care management sector through empowering self-care management and policymaking. The system will effectively impact the progression of diabetes and its complications by early forecasting the risk without the aid of professional physician knowledge which would help to reduce the burden of the disease while saving the expenditures of diabetes mellitus.
- Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., et al. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract, 157, 107843. doi:10.1016/j.diabres.2019.107843Google ScholarCross Ref
- American Diabets Association, (2020). Standards of Medical Care in Diabetes- 2020.Google Scholar
- Barakat, N., Bradley, A. P., & Barakat, M. N. H. (2010). Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE transactions on information technology in biomedicine, 14(4), 1114--1120.Google ScholarDigital Library
- World Health Organization. (2016). Global Report on Diabetes.Google Scholar
- Huang, Y., McCullagh, P., Black, N., & Harper, R. (2007). Feature selection and classification model construction on type 2 diabetic patients' data. Artificial Intelligence in Medicine, 41(3), 251--262. doi:https://doi.org/10.1016/j.artmed.2007.07.002Google ScholarDigital Library
- Balakrishnan, S., Narayanaswamy, R., Savarimuthu, N., & Samikannu, R. (2008). SVM ranking with backward search for feature selection in type II diabetes databases 2008 IEEE International Conference on Systems, Man and Cybernetics (pp. 2628--2633): IEEE.Google Scholar
- Talaei-Khoei, A., & Wilson, J. M. (2018). Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables. International Journal of Medical Informatics, 119, 22--38. doi:https://doi.org/10.1016/j.ijmedinf.2018.08.008Google ScholarCross Ref
- Bashir, S., Qamar, U., & Khan, F. H. (2016). IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework. Journal of Biomedical Informatics, 59, 185--200. doi:https://doi.org/10.1016/j.jbi.2015.12.001Google ScholarDigital Library
- Nai-arun, N., & Moungmai, R. (2015). Comparison of classifiers for the risk of diabetes prediction. Procedia Computer Science, 69, 132--142.Google ScholarCross Ref
- Schwarz, P. E., Li, J., Lindstrom, J., & Tuomilehto, J. (2009). Tools for predicting the risk of type 2 diabetes in daily practice. Hormone and metabolic research, 41(02), 86--97.Google Scholar
- Collins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011). Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine, 9(1), 103. doi:10.1186/1741-7015-9-103Google ScholarCross Ref
- Griffin, S. J., Little, P. S., Hales, C. N., Kinmonth, A. L., & Wareham, N. J. (2000). Diabetes risk score: towards earlier detection of Type 2 diabetes in general practice. DIABETES/METABOLISM RESEARCH AND REVIEWS, 16, 164--171.Google ScholarCross Ref
- Schmidt.M.I., Duncan.B.B., Bang.H., Pankow.J.S., Ballantyne.C.M., Golden.S.H., Folsom. A.R., Chambless L.E. (2005). Identifying Individuals at High Risk for DiabetesGoogle ScholarCross Ref
- Schwarz, P. E., Li, J., Lindstrom, J., & Tuomilehto, J. (2009). Tools for predicting the risk of type 2 diabetes in daily practice. Hormone and metabolic research, 41(02), 86--97.Google Scholar
- Meng, X.-H., Huang, Y.-X., Rao, D.-P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung Journal of Medical Sciences, 29(2), 93--99. doi:https://doi.org/10.1016/j.kjms.2012.08.016Google ScholarCross Ref
- Nai-arun.N., & Moungmai.R. (2015. Comparison of Classifiers for the Risk of Diabetes Prediction. Paper presented at the 7th International Conference on Advances in Information Technology,Google Scholar
- Tao.Z., Wei. X., Liling. X., Xiaoying. H., Ya. Z., Mingrong. Y., et al. (2017). A machine learning-based framework to identify type 2 diabetesthrough electronic health records. Intrenational Journal of Medical Informatics 97, 120--127.Google ScholarCross Ref
- Shahabeddin. A., Sharareh.R. N. K., Mehdi. E., Hajar. H., & Ali. G. (2019). Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthc Inform Res, 25(4), 248--261. doi:10.4258/hir.2019.25.4.248Google ScholarCross Ref
- Norma. L. F., Muhammad.N., Ganjar.A., & Jongtae. R. (2019). Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension. IEEE Access, 7, 144777--144789.Google ScholarCross Ref
- Ozcift, A., & Gulten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer methods and programs in biomedicine, 104(3), 443--451.Google Scholar
- Herman, W. H., Smith, P. J., Thompson, T. J., Engelgau, M. M., & Aubert, R. E. (1995). A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes care, 18(3), 382--387Google ScholarCross Ref
- Kanaya, A. M., Fyr, C. L. W., De Rekeneire, N., Shorr, R. I., Schwartz, A. V., Goodpaster, B. H., et al. (2005). Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. Diabetes care, 28(2), 404--408.Google ScholarCross Ref
- Lindström, J., & Tuomilehto, J. (2003). The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes care, 26(3), 725--731.Google ScholarCross Ref
- M. Norberg.M., E. J. W., Lindahl. B., Andersson. C., Rolandsson. O., & Stenlund.H., W. L. (2006). A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: Journal of Internal Medicine, 263--271.Google ScholarCross Ref
- Schulze.M.B., Hoffmann.K., Boeing.H., Linseisen.J., Rohrmann.S., Mohlig.M., et al. (2007). An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes. diabetes Care, 30(3), 510--515.Google Scholar
- Pasco, J. A., Kotowicz, M. A., Henry, M. J., & Nicholson, G. C. (2010). Evaluating AUSDRISK for predicting incident diabetes in an independent sample of women. The Medical Journal of Australia, 193(6), 374.Google ScholarCross Ref
- Wong.K.C., Brown.A., & Li.S.C.H. (2011). AUSDRISK - application in general practice. Australian family physician, 40, 524--526.Google Scholar
- Cox.J.H., C. C. (2017). Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study. BMJGoogle Scholar
- American Diabetes Association, (n.d). Diagnosis. Retrieved from https://www.diabetes.org/a1c/diagnosisGoogle Scholar
- Diabetes United Kingdom, (n.d)Retrieved from https://riskscore.diabetes.org.uk/start.Google Scholar
- Chen, L.-S., & Cai, S.-J. (2015). Neural-Network-Based Resampling Method for Detecting Diabetes Mellitus. Journal of Medical and Biological Engineering, 35(6), 824--832. doi:10.1007/s40846-015-0093-9Google ScholarCross Ref
- MIT, C. D. (2016). Secondary analysis of electronic health records: Springer International Publishing.Google Scholar
Index Terms
- Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System
Recommendations
Prevalence of sociodemographic factors in a cohort of diabetes mellitus: a retrospective study
ICMHI '22: Proceedings of the 6th International Conference on Medical and Health InformaticsExploring the sociodemographic factors of a cohort is a vital phase in revealing significant aspects of the societal health status. The health care sector utilises the results of exploratory analysis of the sociodemographic nature to fulfil various ...
Matching the Medical Demand in the Context of Online Medical Consultation Data
ICMHI '21: Proceedings of the 5th International Conference on Medical and Health InformaticsIn addition to the benefits of facilitating doctor-patient communication and diagnosis, the wide application of online medical consultation (OMC) platforms is of possible significance to medical demand forecasting. However, studies examining the ...
Self-Management of Diabetes Mellitus with Remote Monitoring: A Retrospective Review of 214 Cases
Purpose: The efficacy of one remote monitoring system was reviewed in order to explore if optimal self-management of diabetes was achieved. Methods: Medical records of 214 patients with diabetes were reviewed from seven diabetes clinics within a single ...
Comments