Developing a classifier to screen for mild cognitive impairment and Alzheimer’s disease using machine learning approaches

Abstract Background Machine learning classifier is a powerful tool that has been recently applied to datasets of Alzheimer’s disease (AD). The majority of approaches developed to classify people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) people have made use of expensive or invasive measurements, discouraging the clinical use of these tools. In the current study, we aimed to develop a classifier to screen for MCI and AD using data that can be routinely collected at low cost (e.g. blood pressure, ApoE genotype). Method Database of the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) was used (n = 14553 data entries). Dataset was split into two parts: training (80%) and testing (20%). A decision tree was first employed to screen for factors (i.e. clinical parameters) that can effectively discriminate CN, MCI, and AD (Step 1). To further refine factor selection, the association between the factors (from screening) and health status (CN, MCI, AD) were assessed using linear and logistic regression methods. Factors that were rejected by the hypothesis test (i.e. non‐obvious relationship with MCI or AD) and those require high cost for collection were removed (Step 2). The remaining factors were used for the construction of the classifier, where decision tree, Gaussian Naïve Bayes and second‐order polynomial approaches were employed. Results The selected factors for classifier construction were ApoE genotype (p<0.001), medical history of stroke (p<0.001 AD, p = 0.223 MCI), diabetes (p<0.001) and depression (p<0.001), and systolic blood pressure (p<0.001). Three models were used to construct the classifier, and the differential accuracy ranges between 74.1 – 84.2% for CN, 25.3 – 50.2% for MCI, and 47.8 – 73.4% for AD ( Table 1 ). Accuracy from second‐order polynomial model was lower than that from the decision tree and Gaussian Naïve Bayes model. Conclusion The classifier developed in the current study is yet to be optimized, especially for MCI. In future study, the performance of our classifier will be evaluated using other AD datasets for generalizability. This classifier can potentially be used by the clinicians as an MCI/AD screening tool for at‐risk population prior to requesting costly diagnostic tests.