Ant Colony Optimized XGBoost for Early Diabetes Detection: A Hybrid Approach in Machine Learning

Le résumé fourni par la source

The primary objective of this research endeavour is to concentrate on the timely detection and prognostication of diabetes and Parkinson's disease through the utilisation of machine learning techniques, specifically the integration of Ant Colony Optimisation (ACO) with the XGBoost algorithm (ACXG). The healthcare issues presented by diabetes and Parkinson's disease underscore the criticality of early detection in order to facilitate effective intervention and enhance patient outcomes. The objective of this work is to establish a connection between the prediction of diabetes and the classification of Parkinson's disease, thereby developing a comprehensive model that improves the prognosis and prevention of these diseases. The project entails the collection and pre-processing of pertinent datasets, afterwards employing a range of classification approaches such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and the innovative ACO-XGBoost model. The results of performance comparisons demonstrate that ACO-XGBoost has superior performance in contrast to conventional approaches. It achieves notable levels of accuracy, precision, recall, F1-score, and AUC, hence establishing its significance as a valuable tool for disease prediction. The incorporation of Ant Colony Optimisation (ACO) with XGBoost (ACXG) showcases the capacity to augment predictive precision and sensitivity, presenting notable progressions in healthcare methodologies. The present study makes a valuable contribution to the advancement of more accurate predictive models, ultimately enhancing the quality of patient care and public health outcomes.

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