Minimal Rule Based Classifier on Diabetic Dataset Using Machine Learning Techniques
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Abstract
Diabetes mellitus is a chronic, lifelong disorder that affects a large number of people. As a result, finding the most relevant clinical registries and performing fast computer-aided pre-diagnoses and diagnoses will become increasingly important in clinical practise. This paper investigates the use of basic rule-based classifiers over a diabetes dataset utilising PCA (Principal Component Analysis) in order to predict diabetic risk and enhance the classification performance of the classifiers. Specifically, PCA will compress the smallest feature correlation among the features and predict the disease in order to enhance classification performance. As a consequence, PCA increases the classification performance while simultaneously decreasing the computation time required by the system. The classification performance of the Pima Indians Diabetes Dataset is examined with and without PCA, and the performance assessment metrics of precision, recall, accuracy, and F1 Score are used to evaluate the classification performance.
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