Machine Learning (ML) is a dynamic method for managing extensive datasets to uncover significant patterns and hidden insights. ML has revolutionized numerous industries, from healthcare to finance, and from entertainment to transportation. Ensemble classifiers combined with Explainable AI (XAI) have surfaced as a significant asset in the field of Alzheimer's Disease (AD) diagnosis. Boosting EC techniques coupled with Shapley Additive Explanations (SHAP) offers a powerful approach to AD diagnosis. This paper investigates boosting ensemble ML schemes, such as XGBoost, LightGBM, and Gradient Boosting (GB), for AD diagnosis and SHAP for feature selection. The proposed scheme achieved efficient results, with an accuracy of more than 94% with minimum features for the detection process.

The kidneys filter waste, toxins, and excess water from the bloodstream, promoting body balance. Chronic kidney disease (CKD) is defined as a progressive deterioration of kidney function. Chronic kidney disease is a serious health condition that affects people all over the world. Early detection of kidney disease is crucial due to the lack of visible symptoms. The timely diagnosis of chronic kidney disease (CKD) significantly impacts the patient’s health development and allows for prompt treatment. The primary goal of this study is to detect the presence or absence of CKD in the human body by analyzing various features obtained from medical tests. So, to accurately detect and diagnose chronic kidney disease (CKD), we use machine learning techniques such as classifiers K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), and Decision Tree (DT). The study used the 400 instances and 25 features of the chronic kidney disease dataset obtained from Kaggle. The elephant herd optimization feature selection algorithm identifies eleven key features. Decision tree outperformed other classification algorithms, achieving an accuracy of 98.75%.