A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model
Abstract
In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.
Downloads
References
Anant, Mohamad Goldust, and Uwe Wollina,"Herpes zoster: A Review of Clinical Manifestations and Management" Viruses Vol.14, No.2,pp.192,2022 .
Patil S, Patil A,” Systemic lupus erythematosus after COVID-19 vaccination: A case report”, J Cosmet Dermatol,Vol.20, No.10,pp.3103-3104,2021.
Pavate, A., Bansode, R. , “Design and Analysis of Adversarial Samples in Safety–Critical Environment: Disease Prediction System”,In: Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore.
A. Pavate and N. Ansari, "Risk Prediction of Disease Complications in Type 2 Diabetes Patients Using Soft Computing Techniques," In: Fifth International Conference on Advances in Computing and Communications (ICACC), Kochi, India, 2015, pp. 371-375,2015.
Pavate, A., Nerurkar, P., Ansari, N., Bansode, R.,”Early Prediction of Five Major Complications Ascends in Diabetes Mellitus Using Fuzzy Logic. In: Soft Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore.
Aruna Pavate., et al. "Diabetic Retinopathy Detection-MobileNet Binary Classifier ," Acta Scientific Medical Sciences Vol.4.No.12 pp.86-91,2020.
Patil, Rohini, Kamal Shah, and Deepak Bhosle. "Impact of COVID-19-related Stress on Glycaemic Control in Hospitalized Patients with Type 2 Diabetes Mellitus." Journal of Health Sciences & Surveillance System Vol.10, No. 4 pp. 397-402,2022.
Patil, R., Shah, K. ,”Performance Evaluation of Machine Learning Classifiers for Prediction of Type 2 Diabetes Using Stress-Related Parameters”. In: Data Science and Security. Lecture Notes in Networks and Systems, vol 462. Springer, Singapore,2022.
Tsang KCH, Pinnock H, Wilson AM, Shah SA. ,”Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review”, J Asthma Allergy. Vol.29, No.15, pp. 855-873,2022.
Dritsas E, Trigka M,”Stroke Risk Prediction with Machine Learning Techniques”, Sensors, Vol.22,No.13, pp.4670,2022.
Kim, J., Mun, S., Lee, S. et al.,” Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea”, BMC Public Health Vol.22, No.664 ,2022.
R. Indrakumari, T. Poongodi, Soumya Ranjan Jena, “Heart Disease Prediction using Exploratory Data Analysis”In:Procedia Computer Science, Vol.173, pp.130-139 2020.
Diabetes http://www.who.int/en/news-room/fact-sheets/detail/diabetes accessed on 21st Feb 2019
IDF SEA members https://www.idf.org/our-network/regions-members/south-east-asia/.../94-india.html assessed on 22nd Feb 2019
ICMR Guidelines for management of type 2 diabetes 2018 https://main.icmr.nic.in/sites/default/files/guidelines/ICMR_GuidelinesType2diabetes2018_0.pdf accessed on 23 July 2021
Patil R, Shah K.,”Machine learning in healthcare: Applications, current status, and future prospects”,In: Handbook of Research on Machine Learning: Foundations and Applications, (1st ed.). Apple Academic Press. 4 August 2022.
Svalastog AL, Donev D, Jahren KN, et al. ,”Concepts and definitions of health and health-related values in the knowledge landscapes of the digital society”, Croat Med J. Vol.58,pp.431-435,2017.
Ahmed N, Ahammed R, Islam M, et al. ,”Machine learning-based diabetes prediction and development of smart web application”,In: International Journal of Cognitive Computing in Engineering, Vol. 2,pp.229-41,2021.
Sivashankari R, Sudha M, Hasan MK, et al.,” An empirical model to predict the diabetic positive using a stacked ensemble approach”, Front. Public Health; Vol.9, 2022.
Diwani SA. Sam A.,” Diabetes forecasting using supervised learning techniques”,Adv. Comp. Sci. Int. J,Vol.3,pp.10-18,2014.
Krishnamoorthi R, Joshi S, Hatim Z, et al.,”A novel diabetes healthcare disease prediction framework using machine learning techniques”, Journal of Healthcare Engineering, 2022.
Mahabub A.,”A robust voting approach for diabetes prediction using traditional machine learning techniques”, SN Applied Sciences, Vol. 1No.1667,2019.
Pima Indians Diabetes dataset. Available from: http://archive.ics.uci.edu/ml/machine learning-databases/pima-indians-diabetes/pima-indians-diabetes data. Accessed: 1st May 2008.
Tigga N, Garg S.,”Prediction of type 2 diabetes using machine learning classification methods classification”,In:International Conference on Computational Intelligence and Data Science, Procedia Computer Science,Vol.167,pp.706-16,2020.
Ullah, N. Javaid, M. U. Javed, Pamir, B. S. Kim, and S. A. Bahaj, "Adaptive Data Balancing Method Using Stacking Ensemble Model and Its Application to Non-Technical Loss Detection in Smart Grids," IEEE Access, vol. 10,pp. 133244–133255, 2022.
Kumari S, Kumar D, Mittal M.,” An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier”, International Journal of Cognitive Computing in Engineering, Vol. 2,pp.40-46, 2021.
Chatrati SP, Hossain G, Goyal A, et al.,” Smart home health monitoring system for predicting type 2 diabetes and hypertension”, J. King Saud Univ. Comput. Inf. Sci, 2020.
Patil R, Shah K.,” Assessment of risk of type 2 diabetes mellitus with stress as a risk factor using classification algorithms”,In: International Journal of Recent Technology and Engineering, Vol. 8,pp. 11273–77,2019.
Saxena R, Sharma SK, Gupta M, et al.,” A novel approach for feature selection and classification of diabetes mellitus: Machine learning methods”, Computational Intelligence and Neuroscience, 2022.
Kannadasan K, Edla D, Kuppili V.,”Type 2 diabetes data classification using stacked autoencoders in deep neural networks”, Clinical Epidemiology and Global Health, pp.530-35, 2019.
Kiranashree BK , Ambika C, Radhika AD. ,”Analysis on machine learning techniques for Stress detection among employees”,Asian Journal of Computer Science and Technology,Vol.10,pp. 35-7, 2021.
Ahuja R. Banga A.,” Mental stress detection in university students using machine learning algorithms”,In:International Conference on Pervasive Computing Advances and Applications (PerCAA 2019), Procedia Computer Science, Vol.152,pp. 349-53, 2019.
Anand A, Shakti S.,” Prediction of diabetes based on personal lifestyle indicators”,In :1st international conference on next generation computing technology:IEEE,2015.
Sisodia D. Sisodia D.,” Prediction of diabetes using classification algorithms” In: Procedia Computer Science, Vol. 132,pp.1578–1585, 2018.
Copyright (c) 2024 Rohini Patil, Anant Patil, Surekha Janrao, Sandip Bankar, Kamal Shah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).