The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm
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Keywords:
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Type 2 Diabetes Mellitus, Cluster Analysis, Fuzzy C-Means Algorithm, Pearson Correlation
Abstract
The prevalence of type 2 diabetes mellitus increases every year. In the long term, type 2 diabetes mellitus can lead to complications of other diseases. This study aimed to analyze the risk cluster for type 2 diabetes mellitus based on risk parameters using the Fuzzy C-Means algorithm. The benefit of analyzing the risk cluster as an initial screening to prevent the occurrence of type 2 diabetes mellitus. This study used 905 subjects’ data consisting of 562 males and 343 females. After the data preprocessing, the optimal number of clusters was determined using a Fuzzy C-Means algorithm process. Subsequently, the Pearson correlation test was conducted to determine the correlation between the risk parameters of type 2 diabetes mellitus and the cluster results. The study resulted in 2 risk clusters, subjects in cluster 1 were older than 60 years (34.1%), had a family history of type 2 diabetes mellitus (62.7%), had hypertension (55.4%), routinely took medicines (73.5%), undertook physical activity for less than half an hour (40.5%), and had a high blood pressure level (53.5%). The Pearson correlation test found that age, regular medication use, hypertension and blood pressure level all seem to have significant correlations with cluster outcomes. The risk cluster of type 2 diabetes mellitus was separated into two clusters using Fuzzy C-Means algorithm, namely the high-risk cluster and the low-risk cluster.
References
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Repaske, D. R. (2016). Medication Induced Diabetes Mellitus. Pediatric Diabetes, 17(6); 392–397
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Sanakal, R. and T. Jayakumari (2014). Prognosis of Diabetes Using Data Mining Approach-fuzzy C Means Clustering and Support Vector Machine. International Journal of Computer Trends and Technology, 11(2); 94 -98
Sarría-Santamera, A., B. Orazumbekova, T. Maulenkul, A. Gaipov, and K. Atageldiyeva (2020). The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review. International Journal of Environmental Research and Public Health, 17(24); 9523
Shan, Z., H. Ma, M. Xie, P. Yan, Y. Guo, W. Bao, Y. Rong, C. L. Jackson, F. B. Hu, and L. Liu (2015). Sleep Duration and Risk Of Type 2 Diabetes: A Meta-analysis of Prospective Studies. Diabetes Care, 38(3); 529–537
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Stahl, C. H., M. Novak, G. Lappas, L. Wilhelmsen, L. Björck, P.-O. Hansson, and A. Rosengren (2012). High-normal Blood Pressure and Long-term Risk Of Type 2 Diabetes: 35-year Prospective Population Based Cohort Study of Men. BMC Cardiovascular Disorders, 12(1); 1–8
Steyn, N. P., J. Mann, P. Bennett, N. Temple, P. Zimmet, J. Tuomilehto, J. Lindström, and A. Louheranta (2004). Diet, Nutrition and The Prevention of Type 2 Diabetes. Public Health Nutrition, 7(1a); 147–165
Tang, M., Y. Zhou, A. Song, J. Wang, Y. Wan, and R. Xu (2021). The Relationship Between Body Mass Index and Incident Diabetes Mellitus in Chinese Aged Population: A Cohort Study. Journal of Diabetes Research, 2021
Tigga, N. P. and S. Garg (2020). Prediction of Type 2 Diabetes Using Machine Learning Classification Methods. Procedia Computer Science, 167; 706–716
Uusitupa, M., T. A. Khan, E. Viguiliouk, H. Kahleova, A. A. Rivellese, K. Hermansen, A. Pfeier, A. Thanopoulou, J. Salas-Salvadó, and U. Schwab (2019). Prevention of Type 2 Diabetes by Lifestyle Changes: A Systematic Review and Meta-analysis. Nutrients, 11(11); 2611
Virani, S. S., A. Alonso, H. J. Aparicio, E. J. Benjamin, M. S. Bittencourt, C. W. Callaway, A. P. Carson, A. M. Chamberlain, S. Cheng, and F. N. Delling (2021). Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association. Circulation, 143(8); e254–e743
Wang, Q., X. Zhang, L. Fang, Q. Guan, L. Gao, and Q. Li (2018). Physical Activity Patterns and Risk of Type 2 Diabetes and Metabolic Syndrome In Middle-aged and Elderly Northern Chinese Adults. Journal of Diabetes Research, 2018
Wei, G. S., S. A. Coady, D. C. Go Jr, F. L. Brancati, D. Levy, E. Selvin, R. S. Vasan, and C. S. Fox (2011). Blood Pressure and the Risk Of Developing Diabetes in African Americans and Whites: Aric, Cardia, and the Framingham Heart Study. Diabetes Care, 34(4); 873–879
Wu, J., S. L. Mackie, and M. Pujades-Rodriguez (2020). Glucocorticoid Dose Dependent Risk of Type 2 Diabetes in Six Immune-mediated Inammatory Diseases: A Population-based Cohort Analysis. BMJ Open Diabetes Research and Care, 8(1); 001220
Zhang, J., Z. Yang, J. Xiao, X. Xing, J. Lu, J. Weng, W. Jia, L. Ji, Z. Shan, and J. Liu (2015). Association Between Family History Risk Categories and Prevalence of Diabetes in Chinese Population. PLoS One, 10(2); e0117044
Zhang, X. and Q. Zhao (2016). Association of Thiazide-Type Diuretics With Glycemic Changes in Hypertensive Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Clinical Trials. The Journal of Clinical Hypertension, 18(4); 342–351
Asamoah-Boaheng, M., O. Sarfo-Kantanka, A. B. Tuour, B. Eghan, and J. C. Mbanya (2019). Prevalence and Risk Factors for Diabetes Mellitus Among Adults in Ghana: A Systematic Review and Meta-analysis. International Health, 11(2); 83–92
Asiimwe, D., G. O. Mauti, and R. Kiconco (2020). Prevalence and Risk Factors Associated With Type 2 Diabetes in Elderly Patients Aged 45-80 Years at Kanungu District. Journal of Diabetes Research, 2020
Association, A. D. (2021). Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2021. Diabetes Care, 44(1); S7–S14
Gökhan, A., C. O. Güzeller, and M. T. Eser (2019). The Effect of The Normalization Method Used in Different Sample Sizes on the Success Of Artificial Neural Network Model. International Journal of Assessment Tools in Education, 6(2); 170–192
Gopalakrishnan, S. and A. Geetha (2017). Study on the Impact of Family History of Diabetes Among Type 2 Diabetes Mellitus Patients in an Urban Area of Kancheepuram District, Tamil Nadu. International Journal Community Medicine and Public Health, 4; 4151–4156
Hinkle, D. E., W. Wiersma, and S. G. Jurs (2003). Applied Statistics for the Behavioral Sciences, volume 663. Houghton Mifflin College Division
Ismail, L., H. Materwala, and J. Al Kaabi (2021). Association of Risk Factors With Type 2 Diabetes: A Systematic Review. Computational and Structural Biotechnology Journal, 19; 1759– 1785
Jamuna, . K. K. M., S. (2020). Prediction of Diabetes and Clustering Based on its Levels Using Fuzzy C Means Algorithm. International Journal of Scientific and Technology, 9(2); 3222–3225
Kim, M.-J., N.-K. Lim, S.-J. Choi, and H.-Y. Park (2015). Hypertension is an Independent Risk Factor For Type 2 Diabetes: the Korean Genome and Epidemiology Study. Hypertension Research, 38(11); 783–789
Kunwar, V., A. S. Sabitha, T. Choudhury, and A. Aggarwal (2019). Chronic Kidney Disease Using Fuzzy C-means Clustering Analysis. International Journal of Business Analytics (IJBAN), 6(3); 43–64
Kurniawan, M., R. R. Muhima, and S. Agustini (2020). Comparison of Clustering K-means, Fuzzy C-means, and Linkage For Nasa Active Fire Dataset. International Journal of Artificial Intelligence & Robotics (IJAIR), 2(2); 34
Liyanage, L. (2018). Diabetes Mellitus and its Risk Factors. EPRA International Journal of Multidisciplinary Research (IJMR), 4(9); 114–117
Lomo, S. I., E. Darmawan, and S. Sugiyarto (2021). Cluster analysis of Type Ii Diabetes Mellitus Patients with the Fuzzy C-means Method. Annals of Mathematical Modeling, 13(1); 32–39
Madhulatha, T. S. (2012). An Overview on Clustering Methods. IOSR Journal of Engineering, 2(4); 719–725 Mezil, . A. B. A., S. A. (2021). Complication of Diabetes Mellitus. Annals of the Romanian Society for Cell Biology, 25(3); 1546–156
Muslimatin, B. (2011). Perbandingan Metode K-means dan Metode Fuzzy C-means untuk Clustering Data: Studi Kasus Pada Harga Saham Harian Pada Pt. Astra, Tbk. Ph.D. thesis, Universitas Islam Negeri Maulana Malik Ibrahim
Park, S. J., Y. J. Park, J. Chang, S. Choi, G. Lee, J. S. Son, K. H. Kim, Y. H. Oh, and S. M. Park (2021). Association Between Antibiotics Use and Diabetes Incidence in A Nationally Representative Retrospective Cohort Among Koreans. Scientific Reports, 11(1); 1–10
Pham, T. B., T. T. Nguyen, H. T. Truong, C. H. Trinh, H. N. T. Du, T. T. Ngo, and L. H. Nguyen (2020). Effects of Diabetic Complications on Health-related Quality of Life Impairment in Vietnamese Patients with Type 2 Diabetes. Journal of Diabetes Research, 2020
Pinidiyapathirage, M., A. Kasturiratne, U. Ranawaka, D. Gunasekara, N. Wijekoon, K. Medagoda, S. Perera, F. Takeuchi, N. Kato, and T. Warnakulasuriya (2013). The Burden of Diabetes Mellitus and Impaired Fasting Glucose in an Urban Population of Sri Lanka. Diabetic Medicine, 30(3); 326–332
Ramya, T. B. (2018). Disease Prediction System Using Fuzzy C-means Algorithm. International Journal of Engineering Research & Technology (IJERT), 6(3); 1–4
Repaske, D. R. (2016). Medication Induced Diabetes Mellitus. Pediatric Diabetes, 17(6); 392–397
Saeedi, P., I. Petersohn, P. Salpea, B. Malanda, S. Karuranga, N. Unwin, S. Colagiuri, L. Guariguata, A. A. Motala, and K. Ogurtsova (2019). Global and Regional Diabetes Prevalence Estimates For 2019 and Projections For 2030 and 2045: Results From The International Diabetes Federation Diabetes Atlas. Diabetes Research and Clinical Practice, 157; 107843
Sakamoto, R., T. Yamakawa, K. Takahashi, J. Suzuki, M. M. Shinoda, K. Sakamaki, H. Danno, H. Tsuchiya, M. Waseda, and T. Takano (2018). Association of Usual Sleep Quality and Glycemic Control in Type 2 Diabetes in Japanese: A Cross Sectional Study. Sleep and Food Registry in Kanagawa (SOREKA). PloS one, 13(1); 0191771
Sanakal, R. and T. Jayakumari (2014). Prognosis of Diabetes Using Data Mining Approach-fuzzy C Means Clustering and Support Vector Machine. International Journal of Computer Trends and Technology, 11(2); 94 -98
Sarría-Santamera, A., B. Orazumbekova, T. Maulenkul, A. Gaipov, and K. Atageldiyeva (2020). The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review. International Journal of Environmental Research and Public Health, 17(24); 9523
Shan, Z., H. Ma, M. Xie, P. Yan, Y. Guo, W. Bao, Y. Rong, C. L. Jackson, F. B. Hu, and L. Liu (2015). Sleep Duration and Risk Of Type 2 Diabetes: A Meta-analysis of Prospective Studies. Diabetes Care, 38(3); 529–537
Simbolon, D., A. Siregar, and R. A. Talib (2020). Physiological Factors and Physical Activity Contribute to The Incidence of Type 2 Diabetes Mellitus in Indonesia. Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal), 15(3)
Stahl, C. H., M. Novak, G. Lappas, L. Wilhelmsen, L. Björck, P.-O. Hansson, and A. Rosengren (2012). High-normal Blood Pressure and Long-term Risk Of Type 2 Diabetes: 35-year Prospective Population Based Cohort Study of Men. BMC Cardiovascular Disorders, 12(1); 1–8
Steyn, N. P., J. Mann, P. Bennett, N. Temple, P. Zimmet, J. Tuomilehto, J. Lindström, and A. Louheranta (2004). Diet, Nutrition and The Prevention of Type 2 Diabetes. Public Health Nutrition, 7(1a); 147–165
Tang, M., Y. Zhou, A. Song, J. Wang, Y. Wan, and R. Xu (2021). The Relationship Between Body Mass Index and Incident Diabetes Mellitus in Chinese Aged Population: A Cohort Study. Journal of Diabetes Research, 2021
Tigga, N. P. and S. Garg (2020). Prediction of Type 2 Diabetes Using Machine Learning Classification Methods. Procedia Computer Science, 167; 706–716
Uusitupa, M., T. A. Khan, E. Viguiliouk, H. Kahleova, A. A. Rivellese, K. Hermansen, A. Pfeier, A. Thanopoulou, J. Salas-Salvadó, and U. Schwab (2019). Prevention of Type 2 Diabetes by Lifestyle Changes: A Systematic Review and Meta-analysis. Nutrients, 11(11); 2611
Virani, S. S., A. Alonso, H. J. Aparicio, E. J. Benjamin, M. S. Bittencourt, C. W. Callaway, A. P. Carson, A. M. Chamberlain, S. Cheng, and F. N. Delling (2021). Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association. Circulation, 143(8); e254–e743
Wang, Q., X. Zhang, L. Fang, Q. Guan, L. Gao, and Q. Li (2018). Physical Activity Patterns and Risk of Type 2 Diabetes and Metabolic Syndrome In Middle-aged and Elderly Northern Chinese Adults. Journal of Diabetes Research, 2018
Wei, G. S., S. A. Coady, D. C. Go Jr, F. L. Brancati, D. Levy, E. Selvin, R. S. Vasan, and C. S. Fox (2011). Blood Pressure and the Risk Of Developing Diabetes in African Americans and Whites: Aric, Cardia, and the Framingham Heart Study. Diabetes Care, 34(4); 873–879
Wu, J., S. L. Mackie, and M. Pujades-Rodriguez (2020). Glucocorticoid Dose Dependent Risk of Type 2 Diabetes in Six Immune-mediated Inammatory Diseases: A Population-based Cohort Analysis. BMJ Open Diabetes Research and Care, 8(1); 001220
Zhang, J., Z. Yang, J. Xiao, X. Xing, J. Lu, J. Weng, W. Jia, L. Ji, Z. Shan, and J. Liu (2015). Association Between Family History Risk Categories and Prevalence of Diabetes in Chinese Population. PLoS One, 10(2); e0117044
Zhang, X. and Q. Zhao (2016). Association of Thiazide-Type Diuretics With Glycemic Changes in Hypertensive Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Clinical Trials. The Journal of Clinical Hypertension, 18(4); 342–351
Authors
Marhamah, Surono, S., & Darmawan, E. . (2023). The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm. Science and Technology Indonesia, 8(1), 17–24. https://doi.org/10.26554/sti.2023.8.1.17-24
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