Detection of Diabetes using Combined ML Algorithm

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Shifat Jahan Setu 1 Fahima Tabassum 2,* Sarwar Jahan 3 Md. Imdadul Islam 1

1. Department of Computer Science & Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

3. Department of Computer Science and Engineering at East West University, Dhaka, Bangladesh

* Corresponding author.


Received: 20 Jul. 2023 / Revised: 17 Sep. 2023 / Accepted: 7 Oct. 2023 / Published: 8 Feb. 2024

Index Terms

FIS, SVM, FCM, Logistic Regression and Combining Algorithm


Recently data clustering algorithm under machine learning are used in ‘real-life data’ to segregate them based on the outcome of a phenomenon. In this paper, diabetes is detected from pathological data of 768 patients using four clustering algorithms: Fuzzy C-Means (FCM), K-means clustering, Fuzzy Inference system (FIS) and Support Vector Machine (SVM). Our main objective is to make binary classification on the data table in a sense that presence or absence of diabetes of a patient. We combined the four machine learning algorithms based on entropy-based probability to enhance accuracy of detection. Before applying combining scheme, we reduce the size of variables applying multiple linear regression (MLR) on the table then logistic regression is again applied on the resultant data to keep the outlier within a narrow range. Finally, entropy based combining scheme with some modification is applied on the four ML algorithms and we got the accuracy of detection about 94% from the combining technique.

Cite This Paper

Shifat Jahan Setu, Fahima Tabassum, Sarwar Jahan, Md. Imdadul Islam, "Detection of Diabetes using Combined ML Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.1, pp.11-23, 2024. DOI:10.5815/ijisa.2024.01.02


[1]Iqbal, H. S., Machine Learning: Algorithms, Real World Applications and Research Directions. SN Computer Science, vol. 2, no. 160, pp.1-12, 2021.
[2]Vraj, S., Urvashi, T. and Ankit S., A Comparative Analysis of Machine Learning Algorithms for Classification Purpose. 4th International Conference on Innovative Data Communication Technology and Application, Elsevier, vol. 215, pp. 422-431, 2022.
[3]Ul Hassan, C., Khan, M. and Shah, M., Comparison of Machine Learning Algorithms in Data classification. 2018 24th International Conference on Automation and Computing (ICAC), Newcastle Upon Tyne, UK, pp. 1-6, 2018.
[4]Danyang, C. and Bingru, Y., An improved k-medoids clustering algorithm. In 2010 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, pp.132-135, 2010.
[5]Harpreet, S., Madan, M., Thomas, M., Zeng-Guang, H., Kum Kum, G., Ashu, S. and Lotfi Z. Real-life applications of fuzzy logic. Hindawi Publishing Corporation Advances in Fuzzy Systems, vol. 2013, Article ID 581879, pp.1-3, 2013.
[6]Maniruzzaman, M., Rahman, M., Ahammed, B. and Abedin M., Classification and prediction of diabetes disease using machine learning paradigm. Health information science and systems, vol. 8(1), pp. 1-14, 2020.
[7]Al-Tarawneh, M., Mustafa, M. and Al-Tarawneh, Z., Hand Movement-Based Diabetes Detection Using Machine Learning Techniques. vol. 9, ISSN 2281-2881, pp. 1-13, 2021.
[8]Ghoushchi, S., Ranjbarzadeh, R., Dadkhah, A., Pourasad, Y. and Bendechache, M., An extended approach to predict retinopathy in diabetic patients using the genetic algorithm and fuzzy C-means. BioMed Research International, vol. 2021, pp. 1-13, 2021.
[9]Devi, R., Bai, A. and Nagarajan N., A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms. Obesity Medicine, Elsevier, vol. 17, 2020.
[10]Dubey, Y., Wankhede, P., Borkar, T., Borkar, A. and Mitra, K., Diabetes Prediction and Classification using Machine Learning Algorithms. 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), Dhaka, Bangladesh, pp. 60-63, 2021.
[11]Islam, N. and Khanam, R., Classification of Diabetes using Machine Learning. International Conference on Computational Performance Evaluation (ComPE), Shillong, India, pp. 185-189, 2021.
[12]Wei, S., Zhao, X. and Miao, C., A comprehensive exploration to the machine learning techniques for diabetes identification. IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, pp. 291-295, 2018.
[13]Dalve, P., Bobby, D., Marathe, A., Dusane, A. and Daga, S., Comparison of Performance of Machine Learning Algorithms for Diabetes Detection. Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, pp. 1-7, 2023.
[14]Karthik, K., Reddy, A., Kulkarni, R. and Mehdi, M., Algorithm Accuracy Verification in Heart Disease Analysis using Machine Learning. 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, pp. 345-349, 2023.
[15]V, C. and Baby, S., Systematic Review on Deep Learning-based Heart Disease Diagnosis. 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, pp. 908-912, 2023.
[16]Kumar, N., Avasthi, S. and Prakash, A., Establishing the Correlation between Parkinson's and Heart Disease using Machine Learning Algorithm. International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India, pp. 581-586, 2023.
[17]Boon, W., Saaveethya, S., King Hann, L., Wong, W. and Filbert, H., Diabetes detection based on machine learning and deep learning approaches. Multimedia Tools and Applications, pp.1-33, 2023.
[18]Shahin A., Khairul, I., A. Arjan, D., Duranta, D., Farija, H. and Habibur, R., A Novel Approach for Best Parameters Selection and Feature Engineering to Analyze and Detect Diabetes: Machine Learning Insights. vol. 2023, pp.1-15, 2023.
[19]Handoyo, M. and Imam, P., The Fuzzy Inference System with Rule Bases Generated by using the Fuzzy C-Means to Predict Regional Minimum Wage in Indonesia. International J. of Opers. and Quant. Management, vol. 24, no. 4, pp. 277-292, 2018.
[20]Memon, K. and Lee, D., Generalized kernel weighted fuzzy C-means clustering algorithm with local information. Fuzzy Sets and Systems, vol. 340, pp. 91-108, 2018.
[21]Rahmani, M., Pal, N. and Arora, K., Clustering of image data using K-means and fuzzy K-means. International Journal of Advanced Computer Science and Applications, vol. 5, pp. 160-163, 2014.
[22]Özaltın, Ö. and Yeniay, Ö., ECG Classification Performing Feature Extraction Automatically Using a Hybrid CNN-SVM Algorithm. 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2021, Ankara, Turkey, pp. 1-5.
[23]Wang, Y. and Wu, Q., Research on Face Recognition Technology Based on PCA and SVM. 7th International Conference on Big Data Analytics (ICBDA), Guangzhou, China, pp. 248-252, 2022.
[24]Tigga O., Pal J. and Mustafi D., A Comparative Study of Multiple Linear Regression and K Nearest Neighbours using Machine Learning. Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, pp. 1-5, 2023.
[25]Gufran, A. A., Salliah, S. B., Mohd, D. A., Sultan, A., Jabeen, N. and Eljialy, A., Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction. Computational and Mathematical Methods in Medicine Volume 2023, Article ID 8191261, pp.1-10.
[26]Kalam A., Anup, M., Juga, D. and Imdadul I., Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules. Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 1140-1150, 2021.