Investigating Rule Induction Methods in Machine Learning for Improving Medical Dementia Prediction

Abstract

Alzheimer’s Disease (AD) is a neurodegenerative disease related to dementia that predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Detecting AD and other conditions like Mild Cognitive Impairment (MCI) can lengthen the lifespan of patients and help them to access the medical services. One approach to achieve a rapid and early diagnosis of AD is using data mining (DM) techniques, which can search various characteristic traits related to Cognitively Normal (CN), AD, and MCI data observations to build classifiers that reveal contributors to the disease. Classifiers developed by DM techniques are used by medical professionals during dementia clinical processes to help make correct diagnosis. In this research, we amend a process based on DM that evaluates characteristics related to dementia conditions, in particular AD. The novelty of the proposed process lies in the classification algorithm that we have named ‘Rules-based Uncertainty Reduction Algorithm’ (RURA). RURA develops classifiers with rules, which strengthen the decisions that can be invoked by the medical professional when evaluating patients with dementia. Empirical evaluation, using several DM algorithms were conducted on biological marker (biomarkers) and behavioral characteristics of data subjects collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data project to analyze the effectiveness of the proposed classification algorithm. The results show that RURA perform over 90% in predicting AD when compared with classifiers developed by other DM algorithms. Furthermore, the results show that delayed word recall and orientation are effective cognitive factors that contribute to the ability to detect dementia early. For the biomarkers, ABETA and neurofibrillary tangles in the neurons showed some associations with AD although fewer than those of the cognitive elements. Moreover, the results obtained show that the classifiers developed by the RURA algorithm from psychological attributes (Subset 2) are higher in accuracy than the other DM algorithms. The results from Subset 2, show that RURA’s classifiers are higher with 11.28%, 1.18%, 1.88%, 3.75%, 1.70%, and 0.27% than PRISM, Nnge, kNN, Naïve Bayes, ORule, and Ridor, separately. Also, RURA developed higher accurate classifiers than the remaining DM algorithms on Subset 12 which includes cognitive attributes and biomarker attributes. However, the performance of RURA did not improve when the general attributes were added to the psychological attributes and the accuracy decreased by 1.34% when mining Subset 2 (psychological attributes and general attributes). More considerably, the results indicate that specific biomarkers in the ADNI-MERGE dataset when used alone by a DM algorithm that we considered for dementia detection often will not result in acceptable predictive systems. For example, when DM algorithms were trained on MRI and PET attributes (Subset 5), the classifiers created had classification accuracies of 42–55% which are relatively low. So, utilizing neuroimaging attributes in the ADNI-MERGE dataset to detect dementia is not ideal using the DM techniques, especially when a baseline visit is used to represent each data subject. Therefore, the biomarkers attributes must be accompanied with psychological elements to improve the detection rate of dementia.