Implementation of Machine Learning Techniques for Detection of Diabetes using Pima-Indians-Diabetes-Dataset
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Abstract
Among people of all ages, including toddlers and the elderly, diabetes is the disease that is spreading the quickest. Diabetes is a chronic illness that might lead to a worldwide healthcare disaster if it spreads widely. Numerous symptoms are associated with diabetes, some of which include increased thirst, increased weariness and sleepiness, decreased appetite, decreased body weight, increased recurring infections, poor eyesight, mood swings, disorientation, and difficulty focusing. In this work, we have used a dataset with a variety of clinical and demographic variables to investigate the performance of Machine Learning techniques for the detection of diabetes using pima-indians-diabetes-dataset. The performance of seven ML techniques (LR, RF, KNN, DT, SVM, AdaBoost, XGBoost, and Naive Bayes) has been evaluated. The research assesses seven distinct machine learning categorization methods in order to identify the system that generates the highest accurate diabetes prediction. We determined that SVM algorithm is best for this dataset in predicting diabetes by contrasting the results of several algorithms.
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