Diabetes Prediction System Using SVM Alogrithm

Snehal Mhatre; Harshada Dixit; Snehal Jagdale; Shital Narsale; Naufil Kazi1

1

Publication Date: 2024/06/07

Abstract: Diabetes Mellitus is a metabolic disease caused by high blood sugar, which can lead to serious health problems if not properly controlled. Early prediction and timely intervention are crucial for preventing and managing diabetes. This paper presents a Diabetic Prediction System utilizing the Support Vector Machine (SVM) algorithm, a powerful machine learning technique known for its effectiveness in classification tasks. The proposed system lever- ages a dataset comprising relevant features such as age, body mass index (BMI), family history, and blood pressure to train the SVM model. Data were preprocessed to control for missing values, normalize features, and reduce bias. The SVM algorithm is employed for classification, as it excels in handling high-dimensional data and is capable of finding optimal hyperplanes to separate different classes. The system undergoes a comprehensive evaluation using performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The results demonstrate the efficacy of the SVM algorithm in accurately predicting the likelihood of diabetes based on the input features.

Keywords: Support Vector Machine (SVM), Prediction System, Machine Learning, Classification, Feature Selection.

DOI: https://doi.org/10.38124/ijisrt/IJISRT24MAY1274

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24MAY1274.pdf

REFERENCES

  1. American Diabetes Association. Economic costs of diabetes in the u.s. in 2020. Diabetes Care, 41(5):917–928, 2021. American Diabetes Association and others. Expert Panel Report Most Popular Articles for Scientific Research. Diabetes care, 26(suppl 1): s5–s20, 2022.
  2. B. Liu, Y. Li, S. Ghosh, Z. Sun, K. Ng, and J. Hu. Risk assessment in diabetes care: Bayesian multitasking and social theory. IEEE Transactions on Knowl- edge and Data Engineering, 32(7):1276–1289, 2020.
  3. B. Kalaiselvi, “Improving random forest distribution based on human relations effectiveness for technology prediction models.,” Measurement, vol. 162, Oct. 2020, Art. no. 107885.
  4. R. Muthukrishnan and R. Rohini, “LASSO: A feature selection technique in predictive modeling for machine learning,” in Proc. IEEE Int. Conf. Adv. Comput. Appl. (ICACA), Oct. 2020, pp. 18–20.
  5. N. Long and S. Dagogo-Jack. Comorbidities of diabetes and high blood pressure: mechanisms and technique to target organ safety. The Journal of Clinical Hypertension, 13(4):244–251, 2021.
  6. W. Engchuan, A. C. Dimopoulos, S. Tyrovolas, F. F. Caballero, A. Sanchez-Niubo, H. Arndt, J. L. Ayuso-Mateos, J. M. Haro, S. Chatterji, and D. B. Panagiotakos. Med. Sci. Monitor Int. Med. J. Exp. Clin. Res., vol. 25, p. 1994, Mar. 2021.
  7. J. Yanase and E. Triantaphyllou, “A systematic survey of laptop-aided prognosis in medicinal drug: beyond and present tendencies,” Expert Syst. Appl., vol. 138, Dec. 2019, Art. no. 112821.
  8. D. Goksuluk, S. Korkmaz, G. Zararsiz, and E. Karaagaoglu, “easyROC: An interactive web-tool for ROC curve analysis using R language environment,” R J., vol. 8, pp. 213–230, Dec. 2021.
  9. Z. He and W. Yu, “Stable function selection for biomarker find out,” Comput. Biol. Chem., vol. 34, no. 4, pp. 215–225, 2020.