A novel light GBM framework combined with gain optimization for predicting dementia severity of Alzheimer’s disease

Introduction Alzheimer’s disease (AD) is a severe brain disorder, which comprises almost 75% of all types of dementia cases. As per the latest report, the global count of Alzheimer’s disease patients is 131 million, due to which the treatment of Alzheimer’s disease is becoming a major burden to the world healthcare system. However, Alzheimer’s disease still remains incurable due to its multifactorial nature of symptoms. Therefore, early-stage diagnosis of Alzheimer’s disease is essential, which helps in the treatment and recovery of patients to a greater extent.Background In the existing literature, few research attempts are carried out for Alzheimer’s disease prediction using machine learning algorithms; yet, most of the existing techniques fail to focus on aspects such as gain-optimisation, dementia severity identification and accuracy enhancement.Methodology To solve these issues, this article presents a light gradient boosting machine-based framework, which employs a gain-optimisation algorithm using multi-category factors for the early stage prediction of Alzheimer’s disease. Further, the proposed framework introduces a detailed feature analysis for identifying the key features needed for the disease prediction and thereby classifies the Alzheimer’s disease patients into different dementia severity types.Results The extensive evaluations of the proposed framework on different dataset sizes using real-world MRI datasets of patients, clearly demonstrate the performance of the proposed work in terms of gain-optimised accuracy metrics. Further, experimental evaluations clearly prove the better performance of the proposed framework when compared to that of other ML techniques including LR, RFC, SVR, SVC and existing methods in terms of Recall, F1-score and accuracy metrics.