Early Detection of Diabetes Using Machine Learning Algorithms and Performance Analysis
DOI:
https://doi.org/10.68337/cpsm.v1.i1.2026-009Keywords:
Diabetes prediction, machine learning, healthcare analytics, disease diagnosis, classification algorithms, predictive modelingAbstract
Diabetes is a widespread metabolic disease and a major public health problem that affects millions of people worldwide. Early screening and risk prediction allow timely intervention for high-risk groups and significantly reduce the risk of severe complications. In recent years, machine learning (ML) has shown clear value in the analysis of large-scale medical data. This study reviews and compares mainstream models for diabetes prediction and proposes a new prediction framework. On the Pima Indians Diabetes Dataset, the proposed hybrid model reached an accuracy of 0.92, a precision of 0.91, a recall of 0.90, and an F1 score of 0.91, higher than the decision tree, support vector machine, random forest, and logistic regression models tested. Such a system may help medical staff with screening and clinical decision-making.
References
[1] KM Jyoti Rani, "Diabetes prediction using machine learning," Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 6, no. 4, pp. 294-305, 2020, doi: 10.32628/CSEIT206463.
[2] G. Swapna, R. Vinayakumar, and K. P. Soman, "Diabetes detection using deep learning algorithms," ICT Express, vol. 4, no. 4, pp. 243-246, 2018, doi: 10.1016/j.icte.2018.10.005.
[3] Satyanarayana Nimmala, Maragoni Mahendar, Pinnapureddy Manasa, H. N. Lakshmi, Medikonda Asha Kiran, and C. Raghavendra, "Fuzzy-enhanced XGBoost model for classifying kidney disease severity," in Proc. 2024 4th Int. Conf. Soft Comput. Secur. Appl. (ICSCSA), 2024, pp. 21-26, doi: 10.1109/ICSCSA64454.2024.00011.
[4] Adurupalli Sony, Manyam Thaile, Medikonda Asha Kiran, Ramesh Babu Pittala, Busa Nithin Kumar, Gandham Sai Chandu, and Muntha Raju, "Predictive health monitoring system for patient risk assessment using machine learning," in Proc. 2025 5th Int. Conf. Intell. Technol. (CONIT), 2025, pp. 1-7, doi: 10.1109/CONIT65521.2025.11167397.
[5] Ewout W. Steyerberg, Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, 2nd ed., Statistics for Biology and Health. Cham, Switzerland: Springer, 2019, doi: 10.1007/978-3-030-16399-0.
[6] Vili Podgorelec, Peter Kokol, Bruno Stiglic, and Ivan Rozman, "Decision trees: An overview and their use in medicine," J. Med. Syst., vol. 26, no. 5, pp. 445-463, 2002, doi: 10.1023/A:1016409317640.
[7] Tegote Roshan, Vaddarapu Tarun Sai, Medikonda Asha Kiran, Sowjanya Yerramaneni, Lakshmi Prasanna Byrapuneni, and A. B. Pavani, "Real time health monitoring system using IoT," in Proc. 2025 5th Int. Conf. Intell. Technol. (CONIT), 2025, pp. 1-6, doi: 10.1109/CONIT65521.2025.11167103.
[8] Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen, "Data-driven decision-making in healthcare: Improving patient outcomes through predictive modeling," Int. J. Sch. Res. Multidiscip. Stud., vol. 5, no. 1, pp. 59-67, 2024, doi: 10.56781/ijsrms.2024.5.1.0040.
[9] Priyanka Rajendra and Shahram Latifi, "Prediction of diabetes using logistic regression and ensemble techniques," Comput. Methods Programs Biomed. Update, vol. 1, Art. no. 100032, 2021, doi: 10.1016/j.cmpbup.2021.100032.
[10] Nahla Barakat, Andrew P. Bradley, and Mohamed Nabil H. Barakat, "Intelligible support vector machines for diagnosis of diabetes mellitus," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 1114-1120, 2010, doi: 10.1109/TITB.2009.2039485.
[11] Pavleen Kaur, Ravinder Kumar, and Munish Kumar, "A healthcare monitoring system using random forest and internet of things (IoT)," Multimedia Tools Appl., vol. 78, no. 14, pp. 19905-19916, 2019, doi: 10.1007/s11042-019-7327-8.
[12] Huanhuan Zhang and Yufei Qie, "Applying deep learning to medical imaging: A review," Appl. Sci., vol. 13, no. 18, Art. no. 10521, 2023, doi: 10.3390/app131810521.
[13] Aishwarya Mujumdar and V. Vaidehi, "Diabetes prediction using machine learning algorithms," Procedia Comput. Sci., vol. 165, pp. 292-299, 2019, doi: 10.1016/j.procs.2020.01.047.
[14] Min Chen, Yixue Hao, Kai Hwang, Lu Wang, and Lin Wang, "Disease prediction by machine learning over big data from healthcare communities," IEEE Access, vol. 5, pp. 8869-8879, 2017, doi: 10.1109/ACCESS.2017.2694446.
[15] J. Manoranjini Kiran, Vijayalaxmi Bindla, G. V. R. Sai Lavanya, and Satyanarayana Nimmala, "A study article on concepts, applications and accomplishments of artificial intelligence: Net App AI," Grenze Int. J. Eng. Technol., vol. 10, no. 2, 2024. [Online]. Available: https://thegrenze.com/pages/servej.php?fn=303.pdf&id=3282&journal=GIJET
[16] Giovanni Briganti and Olivier Le Moine, "Artificial intelligence in medicine: Today and tomorrow," Front. Med., vol. 7, Art. no. 27, 2020, doi: 10.3389/fmed.2020.00027.
[17] Bomma Ramakrishna, Konduri V. N. A. S. M. Prasad, J. E. N. Abhilash, S. Suman, M. S. V. K. V. Prasad, and Medikonda Asha Kiran, "Enhanced emotion recognition via multi-sensor data fusion and machine learning," in Proc. 2025 3rd World Conf. Commun. Comput. (WCONF), 2025, pp. 1-7, doi: 10.1109/WCONF64849.2025.11233351.
[18] Ravi Raju Bandlamudi, Medikonda Asha Kiran, G. Sekhar Reddy, Katika Sushmitha, Nalla Madhuri, and Nadukuda Sanjana, "OsteoPredict: Osteoporosis risk prediction using machine learning," in Proc. 2025 5th Int. Conf. Intell. Technol. (CONIT), 2025, pp. 1-6, doi: 10.1109/CONIT65521.2025.11167375.
[19] Quan Zou, Kaiyang Qu, Yamei Luo, Dehui Yin, Ying Ju, and Hua Tang, "Predicting diabetes mellitus with machine learning techniques," Front. Genet., vol. 9, Art. no. 515, 2018, doi: 10.3389/fgene.2018.00515.
[20] Patlolla Akshaya, Katta Preethi, Medikonda Asha Kiran, Manyam Thaile, Ramesh Babu Pittala, and P. Nagamani, "Multi-sensor fusion for emotion recognition using machine learning," in Proc. 2025 5th Int. Conf. Intell. Technol. (CONIT), 2025, pp. 1-6, doi: 10.1109/CONIT65521.2025.11167520.
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This study used the publicly available Pima Indians Diabetes Dataset (Kaggle).Conference Proceedings Volume
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