A Comprehensive Comparison of Adaboost, Lightgbm, and Catboost for Alzheimer’s Disease Classification

Alzheimer’s disease has been a serious public health issue that needs prompt and precise diagnosis in the management and treatment phase. The following paper presents a machine learning system mainly engineered to classify AD via neuroimaging and clinical features comprising air time1, disp index1, gmrt in air1, max x extension1, and max y extension1 to detect subtle patterns possibly indicative of Alzheimer’s pathology. Systematic data preprocessing and visualization techniques, such as correlation heatmaps and 3D scatter plots, reflect complicated interdependencies in AD progression. The classifiers utilized include Cat Boost and Light GBM for AD and healthy controls, and AdaBoost for mild cognitive impairment. Model performance is measured by accuracy, precision, recall, F1 score, classification reports, and confusion matrices, helping to discover model strengths and weaknesses. A learning curve showed that the models are general and flexible enough for practical circumstances. The work shows that machine learning can integrate multi-modal data modalities into AD diagnosis. Thus, it improves diagnostic accuracy and enables personalized treatment strategies, improving patient outcomes and supporting cutting-edge neurodegenerative disease clinical decisions.