Predicting Diabetes u sing SVM Implemented by Machine Learning
Srikar Sistla
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.
Topics & Concepts
Support vector machineDiabetes mellitusArtificial intelligenceComputer scienceMachine learningTest dataTest (biology)Constant (computer programming)Training setPattern recognition (psychology)Data miningMedicineEndocrinologyBiologyProgramming languagePaleontologyArtificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and LipoproteinsMachine Learning in Healthcare