Models for predicting the quality of life domains on the general population through the orange data mining approach

Bustanul Arifin, Dyah Aryani Perwitasari, Zulkarnain Zulkarnain, M Rifqi Rokhman

Abstract


The incidence of type 2 diabetes mellitus (DM) has been predicted to increase until 2045 in the world. Furthermore, long-term treatment and lifestyle factors affect the quality of life. This study aims to determine the models that can be used to predict the quality-of-life domains in prediabetes patients by using Artificial Intelligent (AI) devices. This is a cross-sectional design in which the inclusion criteria were individuals of age above 18 years and has never been diagnosed with diabetes mellitus (both type 1 DM and type 2 DM), fasted for at least 8 hours, and are willing to sign an informed consent after having received an explanation. Participants were asked to fill out two questionnaires, namely the Indonesian version of the Finnish Diabetes Risk Score (FINDRISC) and the EuroQoL-5 Dimensions-5 Level (EQ-5D-5L). The AI application uses Orange® machine learning with three models used in predictive analysis, such as Logistic Regression, Neural Network, and SVM. In addition, the model was evaluated using the sensitivity, precision, and accuracy of the AU-ROC parameters. The results showed that the neural network model based on the AUC value, precision, accuracy, and also the ROC analysis, was the best for predicting the utility index of domains in the EQ-5D-5L questionnaire, based on demographic data and the FINDRISC questionnaire.


Keywords


Prediction; quality of life; data mining; artificial intelligence

Full Text:

PDF

References


Abadir, A. P., Ali, M. F., Karnes, W., & Samarasena, J. B. (2020). Artificial Intelligence in Gastrointestinal Endoscopy. Clinical Endoscopy, 53(2), 132–141. https://doi.org/10.5946/ce.2020.038

Afzali, M. H., Sunderland, M., Stewart, S., Masse, B., Seguin, J., Newton, N., Teesson, M., & Conrod, P. (2019). Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation. Addiction (Abingdon, England), 114(4), 662–671.

https://doi.org/10.1111/add.14504

Anonymous. (2021a). Badan Pusat Statistik Kabupaten Banggai Laut.

Anonymous. (2021b). Badan Pusat Statistik Kota Malang.

Anonymous. (2021c). Badan Pusat Statistik Provinsi D.I. Yogyakarta.

Anonymous. (2021d). ORANGE, Test and Score. 2021.

Arifin, B., Purba, F. D., Herman, H., Adam, J. M. F., Atthobari, J., Schuiling-Veninga, C. C. M., Krabbe, P. F. M., & Postma, M. J. (2020). Comparing the EQ-5D-3 L and EQ-5D-5 L: studying measurement and scores in Indonesian type 2 diabetes mellitus patients. Health and Quality of Life Outcomes, 18(1). https://doi.org/10.1186/s12955-020-1282-y

Caron, J., Cargo, M., Daniel, M., & Liu, A. (2019). Predictors of Quality of Life in Montreal, Canada: A Longitudinal Study. Community Mental Health Journal, 55(2), 189–201. https://doi.org/10.1007/s10597-018-0340-y

Cho, N., Shaw, J., Karuranga, S., Huang, Y., Fernandes, J. da R., Ohlrogge, A., & B Malanda. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Research and Clinical Practice, 138, 271–281.

https://doi.org/10.1016/j.diabres.2018.02.023

Dall, T. M., Narayan, K. M. V., Gillespie, K. B., Gallo, P. D., Blanchard, T. D., Solcan, M., O’Grady, M., & Quick, W. W. (2014). Detecting type 2 diabetes and prediabetes among asymptomatic adults in the United States: Modeling American Diabetes Association versus US Preventive Services Task Force diabetes screening guidelines. Population Health Metrics, 12(1). https://doi.org/10.1186/1478-7954-12-12

DemÅ¡ar, J., Curk, T., Erjavec, A., Gorup, ÄŒ., HoÄevar, T., MilutinoviÄ, M., Možina, M., Polajnar, M., Toplak, M., StariÄ, A., Å tajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in python. Journal of Machine Learning Research, 14, 2349–2353

EuroQol Research Foundation. (2019). EQ-5D-5L User Guide v3.0. In EuroQol Research Foundation (Issue September). https://euroqol.org/publications/user-guides

Han, E.-R., Yeo, S., Kim, M.-J., Lee, Y.-H., Park, K.-H., & Roh, H. (2019). Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Medical Education, 19(1), 460. https://doi.org/10.1186/s12909-019-1891-5

Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., Bonsel, G., & Badia, X. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736. https://doi.org/10.1007/s11136-011-9903-x

Hosseini, S., & Sardo, S. R. (2021). Data mining tools -a case study for network intrusion detection. Multimedia Tools and Applications, 80(4), 4999–5019. https://doi.org/10.1007/s11042-020-09916-0

Iqbal, Q., Ul Haq, N., Bashir, S., & Bashaar, M. (2017). Profile and predictors of health related quality of life among type II diabetes mellitus patients in Quetta city, Pakistan. Health and Quality of Life Outcomes, 15(1), 142. https://doi.org/10.1186/s12955-017-0717-6

Kolb, H., & Martin, S. (2017). Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. In BMC Medicine (Vol. 15, Issue 1). https://doi.org/10.1186/s12916-017-0901-x

Kriegeskorte, N., & Golan, T. (2019). Neural network models and deep learning. Current Biology : CB, 29(7), R231–R236. https://doi.org/10.1016/j.cub.2019.02.034

Kueh, Y. C., Morris, T., & Ismail, A.-A.-S. (2017). The effect of diabetes knowledge and attitudes on self-management and quality of life among people with type 2 diabetes. Psychology, Health & Medicine, 22(2), 138–144. https://doi.org/10.1080/13548506.2016.1147055

Lindström, J., & Tuomilehto, J. (2003). The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725–731. https://doi.org/10.2337/diacare.26.3.725

Magliano, D. J., Islam, R. M., Barr, E. L. M., Gregg, E. W., Pavkov, M. E., Harding, J. L., Tabesh, M., Koye, D. N., & Shaw, J. E. (2019). Trends in incidence of total or type 2 diabetes: Systematic review. The BMJ, 366. https://doi.org/10.1136/bmj.l5003

Purba, F. D., Hunfeld, J. A. M., Iskandarsyah, A., Fitriana, T. S., Sadarjoen, S. S., Ramos-Goni, J. M., Passchier, J., & Busschbach, J. J. V. (2017). The Indonesian EQ-5D-5L Value Set. PharmacoEconomics, 35(11), 1153–1165. https://doi.org/10.1007/s40273-017-0538-9

Ramesh, A. N., Kambhampati, C., Monson, J. R. T., & Drew, P. J. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334–338. https://doi.org/10.1308/147870804290

Risal, A., Manandhar, S., Manandhar, K., Manandhar, N., Kunwar, D., & Holen, A. (2020). Quality of life and its predictors among aging people in urban and rural Nepal. Quality of Life Research : An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 29(12), 3201–3212. https://doi.org/10.1007/s11136-020-02593-4

Shi, H.-Y., Lee, H.-H., Tsai, J.-T., Ho, W.-H., Chen, C.-F., Lee, K.-T., & Chiu, C.-C. (2012). Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study. PloS One, 7(12), e51285. https://doi.org/10.1371/journal.pone.0051285

Stull, A. J. (2016). Lifestyle Approaches and Glucose Intolerance. In American Journal of Lifestyle Medicine (Vol. 10, Issue 6, pp. 406–416). https://doi.org/10.1177/1559827614554186

Tsai, J.-T., Hou, M.-F., Chen, Y.-M., Wan, T. T. H., Kao, H.-Y., & Shi, H.-Y. (2012). Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR. Supportive Care in Cancer 2012 21:5, 21(5), 1341–1350. https://doi.org/10.1007/S00520-012-1672-8

Wang, R.-H., Wu, L.-C., & Hsu, H.-Y. (2011). A path model of health-related quality of life in type 2 diabetic patients: a cross-sectional study in Taiwan. Journal of Advanced Nursing, 67(12), 2658–2667. https://doi.org/10.1111/j.1365-2648.2011.05701.x

Xiao, Y., Wang, H., Zhang, T., & Ren, X. (2019). Psychosocial predictors of physical activity and health-related quality of life among Shanghai working adults. Health and Quality of Life Outcomes, 17(1), 72. https://doi.org/10.1186/s12955-019-1145-6




DOI: http://dx.doi.org/10.12928/pharmaciana.v12i1.20827

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Bustanul Arifin, Prof. Dr. Dyah Aryani Perwitasari, M.Si., Ph.D., Apt, Zulkarnain Zulkarnain, M Rifqi Rohman

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 
Pharmaciana
ISSN Print: 2088-4559 | ISSN Online: 2477-0256
Website: http://journal.uad.ac.id/index.php/PHARMACIANA
Office: Faculty of  Pharmacy, Universitas Ahmad Dahlan
Jl. Prof. Dr. Soepomo, S.H., Janturan, Warungboto, Umbulharjo, Yogyakarta, Indonesia
Kode pos 55164
Email: pharmaciana@pharm.uad.ac.id