Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances

This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD

Improved Classification Of Medical Data Using Meta-Best Feature Selection

Feature selection provides a useful method for reducing the size of large data sets while maintaining integrity, thereby improving the accuracy of neural networks and other classifiers. However, running multiple feature selection models and their accompanying classifiers can make interpreting results difficult. To this end, we present a data-driven methodology called Meta-Best that not only returns a single feature set related to a classification target, but also returns an optimal size and ranks the features by importance within the set. This proposed methodology is tested on six distinct targets from the well-known REGARDS dataset: Deceased, Self-Reported Diabetes, Light Alcohol Abuse Risk, Regular NSAID Use, Current Smoker, and Self-Reported Stroke. This methodology is shown to improve the classification rate of neural networks by 0.056 using the ROC Area Under Curve metric compared to a control test with no feature selection

Bayesian Variable Selection Methods for Matched Case-Control Studies

Abstract Matched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative

Development of proteomic techniques for biomarker discovery

The main aim of my research, presented here, is to develop proteomic research techniques, for their use in biomarker discovery and identification. This is broken down into three main chapters: • Biomarker Identification in Stroke Brain guided by MALDI-imaging. • Evaluation of the Effectiveness of Heat Treatment for Prevention of Proteomic Sample Degradation using Label Free Relative Quantitation. • Discovery and Identification of Biomarkers for Hypertension Within these chapters special attention is paid to sample preparation, development and assessment of new methods for biomarkers discovery and identification, the importance of experimental design and the application of relevant and useful statistical methods to enable the mining of useful information from rich datasets. The biological changes in stroke induced mouse brain tissue are studied, the prevention of degradation to tissue samples by a novel heat treatment method and changes to plasma samples from hypertensive, wild type and a congenic strain of rat are also studied. The outcomes of this work are multiple, namely: • The identification of a possible marker for stroke from mouse brain tissue • The effect of a new heat treatment device on proteomic data obtained from mouse brain tissue • The novel application of a statistical analysis to a new type of dataset (LC-MS and label free quantitation of biological samples) • The identification of possible biomarkers for hypertension in rat plasma using this metho

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P<0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment