Diabetes Prediction using Support Vector Machine

• Published in: 2024 5th International Conference for Emerging Technology (INCET) pp. 1 - 4
• Main Authors: Sagar, K., Imtiyaz, Sk, Arvindh, A., Nagaraju, A. Shiva, Prasad, Ch. Rajendra, Kumar, P. Kiran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.05.2024
• Subjects: Data mining; Deep learning; Diabetes; Diabetic mellitus (DM); MODY (Maturity Onset Diabetes of the Young); Predictive models; Receiver Operating Characteristic (ROC); Support vector machine (SVM); Support vector machines; University of California Irvine (UCI); Vectors
• DOI: 10.1109/INCET61516.2024.10593364

Diabetes affects people worldwide, in both developed and developing nations, and is a serious health concern. The Worldwide Diabetes Federation reports that 285 million people globally are living with diabetes at this time, and in the next 20 years, that figure is predicted to rise to 380 million. Scientists are working on a very efficient, low-cost way to identify diabetes, which is vital to treat at an early stage. Data mining techniques for accurately predicting diabetes are often tested against the machine learning lab at UCI's Pima Indian diabetes database. In this research, a classifier for diabetes detection using support vector machine (SVM) machine learning technique is proposed. The primary objective is to effectively categorize diabetes from complex medical data. The outcomes of the trial suggest that Support Vector Machine has potential for accurate diabetes diagnosis.