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A COMPARATIVE STUDY OF STATISTICAL AND INTELLIGENT CLASSIFICATION MODELS FOR PREDICTING DIABETES
Keywords:
classification models, logistic regression, regression neural networks.DOI:
https://doi.org/10.17654/0972361723046Abstract
Classification tasks play a pivotal role in various domains, including healthcare, finance, and marketing. Accurate classification models can drive decision-making and provide valuable insights. While logistic regression has been a long-standing method for classification, recent advancements in intelligent models have led to the development of more advanced techniques. This study aims to explore and compare five different classification models: logistic regression, robust logistic regression, adaptive splines regression, k-nearest neighbor and regression neural networks. A comprehensive review of the literature is presented using seven performance measures - MSE, MAE, RMSE, COV, CC, EC and accuracy which have been calculated for these five models. Furthermore, this study provides a foundation for future research on developing more efficient and accurate classification models and investigating advanced ensemble techniques to leverage the strengths of different models.
Received: May 2, 2023
Accepted: June 13, 2023
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