Predicting Diabetes u sing SVM Implemented by Machine Learning

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 s...

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Bibliographic Details
Published inInternational journal of soft computing and engineering Vol. 12; no. 2; pp. 16 - 18
Main Author Sistla, Srikar
Format Journal Article
LanguageEnglish
Published 30.05.2022
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Summary: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.
ISSN:2231-2307
2231-2307
DOI:10.35940/ijsce.B3557.0512222