Early Diabetic Prediction System using Machine Learning

Archana Jain; Suraj Malik; Akshay Raj

Authors

Archana Jain Research Scholar, Department of Computer Science and Engineering, School of Engineering & Technology, IIMT University, India
Suraj Malik Associate Professor, Department of Computer Science and Engineering, School of Engineering & Technology, IIMT University, India
Akshay Raj Assistant Professor, Department of Research & Development, IIMT University, India

Keywords:

Machine Learning, Chronic Disease Prediction, Early Detection, Healthcare Diagnostics, Classification Algorithms, Predictive Modelling, Data Mining, Algorithm Evaluation

Abstract

The goal of this project is to create a machine learning-based system that is inclusive and can predict many chronic illnesses, including diabetes, heart disease, and chronic kidney disease. To determine which machine learning classification technique is best for disease prediction, this study uses a variety of models, including K-Nearest Neighbours, Support Vector Machines, Decision Trees, Random Forests, and Logistic Regression. These models are tested using numerous disease-specific datasets to ensure accuracy, sensitivity, and specificity. It is important to identify chronic diseases early to improve patient outcomes and reduce mortality. The present work aims to develop predictive models to identify at-risk individuals for chronic conditions by analysing patient data, including medical history, demographics, and clinical measurements. The end product would be a web application that supports early diagnosis, better patient care, and the effective use of healthcare resources. This research helps in proving the capability of machine learning to diagnose chronic diseases at an early stage.

References

L. P. Nguyen, D. D. Tung, D. T. Nguyen, H. N. Le, T. Q. Tran, T. V. Binh, and D. T. N. Pham, “The utilization of machine learning algorithms for assisting physicians in the diagnosis of diabetes,” vol. 13, no. 12, p. 2087, 2023.

C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective heart disease prediction using machine learning techniques,” Algorithms, vol. 16, no. 2, p. 88, 2023.

C.-Y. Chou, D.-Y. Hsu, and C.-H. Chou, “Predicting the onset of diabetes with machine learning methods,” Journal of Personalized Medicine, vol. 13, no. 3, p. 406, 2023.

J. Rashid, S. Batool, J. Kim, M. W. Nisar, A. Hussain, S. Juneja, and R. Kushwaha, “An augmented artificial intelligence approach for chronic diseases prediction,” Frontiers in Public Health, vol. 10, no. 3, p. 860396, 2022.

V. Singh, V. K. Asari, and R. Rajasekaran, “A deep neural network for early detection and prediction of chronic kidney disease,” Diagnostics, vol. 12, no. 1, p. 116, 2022.

T. Alekya, D. Narendar, D. Mahipal, N. Arjun, and B. Nagaraj, “Design and Evaluation of Chronomodulated Drug Delivery of Tramadol Hydrochloride,” Drug Res. (Stuttg)., vol. 68, no. 3, pp. 174–180, Mar. 2018.

M. R. Donthi, A. Butreddy, R. N. Saha, P. Kesharwani, and S. K. Dubey, “Leveraging spray drying technique for advancing biologic product development–A mini review,” Heal. Sci. Rev., vol. 10, p. 100142, Mar. 2024.

G. Konnurmath and S. Chickerur, “An investigation into power aware aspects of rendering 3D models on multi-core processors,” Procedia Computer Science, vol. 218, pp. 887–898, 2023.

G. Konnurmath and S. Chickerur, “GPU shader analysis and power optimization model,” Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12925–12930, Feb. 2024.

G. Konnurmath and S. Chickerur, “Power-aware characteristics of matrix operations on multicores,” Applied Artificial Intelligence, vol. 35, no. 15, pp. 2102–2123, 2021.

G. Konnurmath and S. Chickerur, “Real-time frame rate controlled dynamic voltage and frequency scaling for GPU power consumption optimization,” AIP Conference Proceedings, vol. 3131, no. 1, p. 020006, Sep. 2024.

V. Yadav, “Machine Learning in Managing Healthcare Workforce Shortage: Analyzing how Machine Learning can Optimize Workforce Allocation in Response to Fluctuating Healthcare Demands,” Progress In Medical Sciences, pp. 1–9, Aug. 2023.

V. Yadav, “Machine Learning for Predicting Healthcare Policy Outcomes: Utilizing Machine Learning to Forecast the Outcomes of Proposed Healthcare Policies on Population Health and Economic Indicators,” Journal of Artificial Intelligence & Cloud Computing, vol. 1, no. 2, pp. 1–10, Jun. 2022.

R. Aravindhan, R. Shanmugalakshmi, K. Ramya and Selvan C., "Certain investigation on web application security: Phishing detection and phishing target discovery," 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2016, pp. 1-10.

R. Aravindhan and R. Shanmugalakshmi, "Comparative analysis of Web 3.0 search engines: A survey report," 2013 International Conference on Advanced Computing and Communication Systems, Coimbatore, India, 2013, pp. 1-6.

T. K. Lakshmi and J. Dheeba, "Classification and Segmentation of Periodontal Cyst for Digital Dental Diagnosis Using Deep Learning," Computer Assisted Methods in Engineering and Science, vol. 30, no. 2, pp. 131-149, 2023.

T. K. Lakshmi and J. Dheeba, "Digital Decision Making in Dentistry: Analysis and Prediction of Periodontitis Using Machine Learning Approach," International Journal of Next-Generation Computing, vol. 13, no. 3, 2022.

T. K. Lakshmi and J. Dheeba, "Digitalization in Dental Problem Diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis," International Journal of Recent Technology and Engineering, vol. 8, no. 5, pp. 67-74, 2020.

T. K. Lakshmi and J. Dheeba, "Predictive Analysis of Periodontal Disease Progression Using Machine Learning: Enhancing Oral Health Assessment and Treatment Planning," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 10s, pp. 660–671, 2023.

Agussalim, Rusli, A. Rasjid, M. Nur, T. Erawan, Iwan, and Zaenab, "Caffeine in student learning activities," J. Drug Alcohol Res., vol. 12, no. 9, Ashdin Publishing, 2023.

P. P. Anand, U. K. Kanike, P. Paramasivan, S. S. Rajest, R. Regin, and S. S. Priscila, “Embracing Industry 5.0: Pioneering Next-Generation Technology for a Flourishing Human Experience and Societal Advancement,” FMDB Transactions on Sustainable Social Sciences Letters, vol.1, no. 1, pp. 43–55, 2023.

G. Gnanaguru, S. S. Priscila, M. Sakthivanitha, S. Radhakrishnan, S. S. Rajest, and S. Singh, “Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images,” in Advances in Medical Technologies and Clinical Practice, IGI Global, pp. 46–65, 2024.

G. Gowthami and S. S. Priscila, “Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach,” Int. J. Crit. Comput.-based Syst., vol. 10, no. 4, pp. 355–374, 2023.

A. J. Obaid, S. Suman Rajest, S. Silvia Priscila, T. Shynu, and S. A. Ettyem, “Dense convolution neural network for lung cancer classification and staging of the diseases using NSCLC images,” in Proceedings of Data Analytics and Management, Singapore; Singapore: Springer Nature, pp. 361–372, 2023.

S. S. Priscila and A. Jayanthiladevi, “A study on different hybrid deep learning approaches to forecast air pollution concentration of particulate matter,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023.

S. S. Priscila, S. S. Rajest, R. Regin, and T. Shynu, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.

S. S. Priscila and S. S. Rajest, “An Improvised Virtual Queue Algorithm to Manipulate the Congestion in High-Speed Network”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 343–360, 2022.

S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and S. András, “Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1–11, 2023.

R. Regin, Shynu, S. R. George, M. Bhattacharya, D. Datta, and S. S. Priscila, “Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting,” Int. J. Bioinform. Res. Appl., vol. 19, no. 3, 2023.

S. Silvia Priscila, S. Rajest, R. Regin, T. Shynu, and R. Steffi, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.

S. S. Rajest, S. Silvia Priscila, R. Regin, T. Shynu, and R. Steffi, “Application of Machine Learning to the Process of Crop Selection Based on Land Dataset,” International Journal on Orange Technologies, vol. 5, no. 6, pp. 91–112, 2023.

T. Shynu, A. J. Singh, B. Rajest, S. S. Regin, and R. Priscila, “Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology,” International Journal of Intelligent Engineering Informatics, vol. 10, no. 6, pp.484-503, 2022.

S. S. Priscila, D. Celin Pappa, M. S. Banu, E. S. Soji, A. T. A. Christus, and V. S. Kumar, “Technological frontier on hybrid deep learning paradigm for global air quality intelligence,” in Cross-Industry AI Applications, IGI Global, pp. 144–162, 2024.

S. S. Priscila, E. S. Soji, N. Hossó, P. Paramasivan, and S. Suman Rajest, “Digital Realms and Mental Health: Examining the Influence of Online Learning Systems on Students,” FMDB Transactions on Sustainable Techno Learning, vol. 1, no. 3, pp. 156–164, 2023.

S. R. S. Steffi, R. Rajest, T. Shynu, and S. S. Priscila, “Analysis of an Interview Based on Emotion Detection Using Convolutional Neural Networks,” Central Asian Journal of Theoretical and Applied Science, vol. 4, no. 6, pp. 78–102, 2023.

Agussalim, S. N. Fajriah, A. Adam, M. Asikin, T. Podding, and Zaenab, "Stimulant drink of the long driver lorry in Sulawesi Island, Indonesia," J. Drug Alcohol Res., vol. 13, no. 3, Ashdin Publishing, 2024.

Aravindhan, R., Shanmugalakshmi, R. & Ramya, K. Circumvention of Nascent and Potential Wi-Fi Phishing Threat Using Association Rule Mining. Wireless Pers Commun 94(3), 2331–2361, 2017.

R. Aravindhan and R. Shanmugalakshmi, "Visual analytics for semantic based image retrieval (SBIR): semantic tool," International Journal of Latest Trends in Engineering and Technology, vol. 7, no. 2, pp. 300–312, 2016.

R. Aravindhan and R. Shanmugalakshmi, "Multistage fuzzy classifier based phishing detection using LDA and CRF features followed by impersonated entity discovery," International Journal of Control Theory and Applications, vol. 10, no. 29, pp. 33–42, 2017.

Selvan, C., Ragunathan, A., & Ashwinkumar, U. M. (2024). Mitigating phishing threats in unmanned aircraft systems (UAS) through multi-stage defense strategies. In Analyzing and Mitigating Security Risks in Cloud Computing (pp. 125–162). IGI Global.

R. Boina, “Assessing the Increasing Rate of Parkinson’s Disease in the US and its Prevention Techniques”,” International Journal of Biotechnology Research and Development, vol. 3, no. 1, pp. 1–18, 2022.

R. M. Badiger, R. Yakkundimath, G. Konnurmath, and P. M. Dhulavvagol, “Deep learning approaches for age-based gesture classification in South Indian Sign Language,” Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13255–13260, Apr. 2024.

V. S. Jadhav, R. Yakkundimath, and G. Konnurmath, “Cervical cancer severity characterization using machine learning techniques,” Indian Journal of Gynecologic Oncology, vol. 22, p. 156, 2024.

S. K. Sehrawat, "Transforming Clinical Trials: Harnessing the Power of Generative AI for Innovation and Efficiency," Transactions on Recent Developments in Health Sectors, vol. 6, no. 6, pp. 1-20, 2023.

S. K. Sehrawat, "Empowering the Patient Journey: The Role of Generative AI in Healthcare," International Journal of Sustainable Development Through AI, ML and IoT, vol. 2, no. 2, pp. 1-18, 2023.

S. K. Sehrawat, "The Role of Artificial Intelligence in ERP Automation: State-of-the-Art and Future Directions," Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023.

R. Rajitha, D. Narendar, N. Arjun, B. Nagaraj, and D. Mahipal, “Colon Delivery of Naproxen: Preparation, Characterization and Clinical Evaluation in Healthy Volunteers,” Int. J. Pharm. Sci. Nanotechnology(IJPSN), vol. 9, no. 3, pp. 1–10, 2016.

S. Karnam, M. R. Donthi, A. B. Jindal, and A. T. Paul, “Recent innovations in topical delivery for management of rheumatoid arthritis: A focus on combination drug delivery,” Drug Discov. Today, vol. 29, no. 8, p. 104071, Aug. 2024.

M. Choudhari, M. R. Donthi, S. Damle, G. Singhvi, R. N. Saha, and S. K. Dubey, “Implementation of Quality by Design Approach for Optimization of RP-HPLC Method for Quantification of Abiraterone Acetate in Solid Dispersion in Forced Degradation Studies,” Curr. Chromatogr., vol. 9, no. 1, pp. 78–94, Nov. 2022.