Predicting Diabetes using SVM Implemented by Machine Learning
Srikar Sistla
Srikar Sistla*, Department of Computer Science, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram (Tamil Nadu), India. 

Manuscript received on 21 April 2022. | Revised Manuscript received on 27 April 2022. | Manuscript published on 30 May 2022. | PP: 16-18 | Volume-12 Issue-2, May 2022. | Retrieval Number: 100.1/ijsce.B35570512222 | DOI: 10.35940/ijsce.B3557.0512222
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Abstract: Age, BMI, and insulin levels, which play important roles because they are not constant and do not follow any specific patterns, are some of the factors that can be used to identify the chronic disease of Diabetes. Besides the elements described above, a few additional will be studied in subsequent subjects in this study. Before cleaning the data, support vector machine (SVM) algorithms, pandas, NumPy, and sci-kit-learn libraries are used to predict the patient’s diagnosis and classify the data into various categories. The output contains two parameters: DIABETIC and NON-DIABETIC. With the available dataset, the accuracy score of training data was 77.5 percent and the accuracy score of test data was 80.5 percent. 
Keywords: Medical Diagnosis; Diabetes; Medical Computing; Machine Learning; Support Vector Machines.
Scope of the Article: Machine Learning