Ensemble Learning with Time Accumulative Effect for Early Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder. Early diagnosis of AD is critical for disease management and treatment options to slow progression. The existing early diagnosis algorithms for AD ignore the distinct time accumulative effect seen in chronic diseases and do not address the problem of adaptation of multi-source heterogeneous data to a single learner. We use the idea of ensemble learning to train multi-source heterogeneous data using different learners to solve the problem. The time accumulative operator is fusing while being trained. The outcomes of many learners are then combined using the decision fusion approach. Experimental results demonstrate that our algorithmic framework attains an average accuracy of 75.75% and the time accumulative effect also benefits our model.