Exploring the Impact of Filtering Techniques on the Performance of Different Classifiers for Predicting Diabetes Mellitus

Diabetes is a chronic disorder that affects how many people around the world process their metabolism. The prevalence of diabetes is increasing alarmingly every year. Diabetes can harm various essential organs in the body if it is not managed well. Therefore, it is very important to identify diabetes early and initiate treatment as soon as possible to avoid the disease from causing such complications. This research applied five different techniques in WEKA tools to forecast diabetes based on the input attributes of the dataset. This research used 17 attributes, such as Age, Sex, Polyuria, Polydipsia and other medical terms to assess the likelihood of a patient developing disease. Nowadays Filtering techniques are crucial in various fields and applications, serving to extract relevant information, enhance data quality, and improve overall system performance. The filters—Resample, Obfuscate, and Discretizeare important for preprocessing data for machine learning algorithms. They help to overcome challenges related to data imbalance, privacy protection, and data representation, resulting in enhanced model performance and data usability. These filters are vital for achieving high-quality data preprocessing, feature engineering, and privacy preservation in machine learning workflows. Preprocessed data applied to the classification techniques, namely JRip, SMO, KNN, Logistic and SGD used to examine the diabetes. The performance measurement for this study are the mean absolute error and kappa statistics and the accuracy of correct classification, of the classifier. The results show that Overall, employing these filters in the preprocessing stage assists to optimize data preparation and establishes the basis for successful machine learning outcomes