The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Na？ve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.
References
[1]
Kumar, D., Mandal, N. and Kumar, Y. (2022) Fog-Based Framework for Diabetes Prediction Using Hybrid ANFIS Model in Cloud Environment. Personal and Ubiquitous Computing, 27, 909-916. https://doi.org/10.1007/s00779-022-01678-w
[2]
Chaturvedi, S. (2023) Clinical Prediction on ML Based Internet of Things for E-Health Care System. International Journal of Data Informatics and Intelligent Computing, 2, 29-37. https://doi.org/10.59461/ijdiic.v2i3.76
[3]
Bhatia, M., Kaur, S., Sood, S.K. and Behal, V. (2020) Internet of Things-Inspired Healthcare System for Urine-Based Diabetes Prediction. Artificial Intelligence in Medicine, 107, Article 101913. https://doi.org/10.1016/j.artmed.2020.101913
[4]
Hemalatha, P.K. and Choubey, S.B. (2024) Clinical Prediction Using Machine Learning-Based IoT for E-Healthcare Systems. International Journal of Advanced Research in Engineering and Technology, 1, 49-58.
[5]
Kaur, P., Sharma, N., Singh, A. and Gill, B. (2018) CI-DPF: A Cloud IoT Based Framework for Diabetes Prediction. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, 1-3 November 2018, 654-660. https://doi.org/10.1109/iemcon.2018.8614775
[6]
Hasan, S., Wang, J., Anwar, M.S., Zhang, H., Liu, Y. and Yang, L. (2024) Investigating the Potential of VR in Language Education: A Study of Cybersickness and Presence Metrics. 2024 13th International Conference on Educational and Information Technology, Chengdu, 22-24 March 2024, 189-196. https://doi.org/10.1109/iceit61397.2024.10540709
[7]
Abdollahi, J., Babak, N.M. and Mehdi, E.P. (2019) Improving Diabetes Diagnosis in Smart Health Using Genetic-Based Ensemble Learning Algorithm. Approach to IoT Infrastructure. Future Generation in Distributed Systems, 1, 23-30.
[8]
Verma, A., Agarwal, G., Gupta, A.K. and Sain, M. (2021) Novel Hybrid Intelligent Secure Cloud Internet of Things Based Disease Prediction and Diagnosis. Electronics, 10, Article 3013. https://doi.org/10.3390/electronics10233013
[9]
Yıldırım, E., Cicioğlu, M. and Çalhan, A. (2023) Fog-Cloud Architecture-Driven Internet of Medical Things Framework for Healthcare Monitoring. Medical & Biological Engineering & Computing, 61, 1133-1147. https://doi.org/10.1007/s11517-023-02776-4
[10]
Nagaraj, P. and Deepalakshmi, P. (2022) An Intelligent Fuzzy Inference Rule-Based Expert Recommendation System for Predictive Diabetes Diagnosis. International Journal of Imaging Systems and Technology, 32, 1373-1396. https://doi.org/10.1002/ima.22710
[11]
Dewangan, N.K., Pandey, N., Gautam, R., Goswami, A.K., Mitkari, S.R., Singh, A., et al. (2024) A Novel Healthcare Decision Support System Using IoT and ANFIS. International Journal of Information Technology, 1-7. https://doi.org/10.1007/s41870-024-02159-4
[12]
Padhy, S., Dash, S., Routray, S., Ahmad, S., Nazeer, J. and Alam, A. (2022) IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction. Computational Intelligence and Neuroscience, 2022, 1-11. https://doi.org/10.1155/2022/2389636
[13]
Parvathy S., and Sridevi, S. (2022) Secure Deep Learning Model for Disease Prediction and Diagnosis System in Cloud Based IoT. AIP Conference Proceedings, 2463, Article 20008. https://doi.org/10.1063/5.0080304
[14]
Giansanti, D. (2024) Transforming Precision Medicine: The Intersection of Digital Health and AI. MDPI AG, 368 p.
[15]
Kumar, A., Satyanarayana Reddy, S.S., Mahommad, G.B., Khan, B. and Sharma, R. (2022) Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework. Scientific Programming, 2022, 1-11. https://doi.org/10.1155/2022/2090681
[16]
Sumarlinda, S.R.I., et al. (2024) The Improvement Prediction Model Using ANFIS for Medical Dataset. Journal of Theoretical and Applied Information Technology, 102, 1663-1672.
[17]
Sharma, N. and Singh, A. (2019) Diabetes Detection and Prediction Using Machine Learning/IoT: A Survey. Advanced Informatics for Computing Research: Second International Conference, Shimla, 14-15 July 2018.
[18]
Nadeem, M.W., Goh, H.G., Ponnusamy, V., Andonovic, I., Khan, M.A. and Hussain, M. (2021) A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes. Healthcare, 9, Article 1393. https://doi.org/10.3390/healthcare9101393
[19]
Abdollahi, J., Moghaddam, B.N. and Parvar, M.E. (2019) Improving Diabetes Diagnosis in Smart Health Using Genetic-based Ensemble learning algorithm Approach to IoT Infrastructure. Future Generation in Distributed Systems Journal, 1, 26-33.
[20]
Awotunde, J.B., Jimoh, R.G., Ogundokun, R.O., Misra, S. and Abikoye, O.C. (2022) Big Data Analytics of IoT-Based Cloud System Framework: Smart Healthcare Monitoring Systems. In: Internet of Things, Springer, 181-208. https://doi.org/10.1007/978-3-030-80821-1_9
[21]
Mahboob Alam, T., Shaukat, K., Khelifi, A., Ahmad Khan, W., Muhammad Ehtisham Raza, H., Idrees, M., et al. (2022) Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System. Computers, Materials & Continua, 70, 5305-5319. https://doi.org/10.32604/cmc.2022.020344
[22]
Ramesh, A., Subbaraya, C.K. and Krishnegowda, R.K.G. (2023) A Remote Health Monitoring Framework for Heart Disease and Diabetes Prediction Using Advanced Artificial Intelligence Model. Indonesian Journal of Electrical Engineering and Computer Science, 30, 846-859. https://doi.org/10.11591/ijeecs.v30.i2.pp846-859
[23]
Divya, N.J., Kanniga Devi, R. and Muthukannan, M. (2024) Privacy-Aware IoT-Based Multi-Disease Diagnosis Model for Healthcare System. In: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem, CRC Press, 376-406. https://doi.org/10.1201/9781003429609-22
[24]
Kumar, A., Manasvee, and Jha, P. (2022) Fuzzy Logic Applications in Healthcare. In: Next Generation Communication Networks for Industrial Internet of Things Systems, CRC Press, 1-25. https://doi.org/10.1201/9781003355946-1
[25]
Ramkumar, G., Seetha, J., Priyadarshini, R., Gopila, M. and Saranya, G. (2023) IoT-Based Patient Monitoring System for Predicting Heart Disease Using Deep Learning. Measurement, 218, Article 113235. https://doi.org/10.1016/j.measurement.2023.113235
[26]
Tang, Z., Tang, Z., Liu, Y., Tang, Z. and Liao, Y. (2024) Smart Healthcare Systems: A New IoT-Fog Based Disease Diagnosis Framework for Smart Healthcare Projects. Ain Shams Engineering Journal, 15, Article 102941. https://doi.org/10.1016/j.asej.2024.102941
