Predicting Neurodegenerative Diseases Using a Novel Blood Biomarkersbased Model by Machine Learning This paper presents machine learning based framework to the analysis and modeling of several neurodegenerative diseases by using features from bloodbased biomarkers. The proposed approaches can be employed for early detection of Alzheimerxe2x80x99s disease (AD) or Parkinsonxe2x80x99s disease (PD). In particular, we applied LDA (linear discriminant analysis) for visualizing the dataset as 2D or 3D scatter plots. Moreover, we constructed various classifiers for several different tasks of classification, and explore the accuracy of these classifiers. Based on our experiments, random forests are in general a very good choice of these tasks considering both the computing time (during modeling and prediction) and the accuracy.

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateoftheart convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimeru0027s Disease (AD) and Parkinsonu0027s Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying crossvalidation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.