Predicting Diabetes u sing SVM Implemented by Machine Learning | Zendy

Srikar Sistla | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Predicting Diabetes u sing SVM Implemented by Machine Learning

Author(s) -

Srikar Sistla

Publication year - 2022

Publication title -

international journal of soft computing and engineering

Language(s) - English

Resource type - Journals

ISSN - 2231-2307

DOI - 10.35940/ijsce.b3557.0512222

Subject(s) - support vector machine , diabetes mellitus , artificial intelligence , computer science , machine learning , test data , test (biology) , constant (computer programming) , training set , pattern recognition (psychology) , data mining , medicine , endocrinology , paleontology , biology , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research