Diagnostics and Assessment of the Severity of Alzheimer’s Disease: Machine Learning Algorithms Based on Markers of Inflammation

Background: as the most common form of dementia, Alzheimer’s disease (AD) is characterized by cognitive deterioration and usually begins with loss of memory of recent events. It is important to search for biological, sensitive and affordable methods that could be used for early diagnostics of AD and determine the severity of the disease.Objective: to develop machine learning algorithms based on such inflammatory markers as the enzymatic activity of leukocyte elastase (LE) and the functional activity of the α1-proteinase inhibitor (α1-PI) for diagnosing and assessing the severity of AD.Patients and methods: the study included128 people aged 55 to 94 years (73.7 ± 7.9 years), of which 91 patients were diagnosed with Alzheimer’s disease and 37 apparently healthy people (control). The indicators of LE and α1-PI in blood plasma were used as classifying features for building models. The following algorithms were used to build a machine learning model: Optimal Valid Partition (OVP), logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and statistically weighted syndromes (WSWS). The predictive performance of the constructed classiers was evaluated by the overall accuracy (accuracy), sensitivity (sensitivity), specificity (specificity), F-measure and ROC-analysis.Results: the developed machine learning algorithms made it possible to reliably divide the general group of subjects (patients + conditionally healthy), as well as patients with different AD severity, into 4 quadrants of a two-dimensional diagram in the LE and α1-PI coordinates and showed close and fairly high predictive efficiency.Conclusion: the developed machine learning algorithms have proven close and sufficiently high prognostic efficacy for assessing the severity of AD based on inflammatory markers (enzymatic activity of LE and functional activity of α1-PI) and, probably, can be useful for early diagnostics of the disease and timely administration of therapy.