Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data

2018 ◽  
Vol 13 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Fabiana Novellino ◽  
Stefania Barone ◽  
Tiziana Tallarico ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms

scholarly journals A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Xun-Heng Wang ◽  
Lihua Li
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Clinical Symptoms ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Phase Synchrony ◽  
Unified Framework ◽  
Brain Behavior ◽  
Behavior Models

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.

255. Automated classification of Resting State fMRI networks using Machine Learning algorithms

2018 ◽  
Vol 56 ◽  
pp. 219-220 ◽  
Author(s):  
C. Pinardi ◽  
O. Ortenzia ◽  
S. Gardini ◽  
R. Aldigeri ◽  
M. Micheli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Automated Classification

scholarly journals Machine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI

2021 ◽  
Vol 9 (VI) ◽  
pp. 5399-5409
Author(s):  
K. Emily Esther Rani
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet

Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.

scholarly journals Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Amir H. Gandomi ◽  
Mieke H. J. Schulte ◽  
Anna E. Goudriaan ◽  
Simon Y. Foo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Decision Tree ◽  
Data Reduction ◽  
Resting State ◽  
Naive Bayes ◽  
Resting State Fmri ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayes Classifier

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

scholarly journals Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

2021 ◽  
Vol 11 (8) ◽  
pp. 1049
Author(s):  
Katrin Trentzsch ◽  
Paula Schumann ◽  
Grzegorz Śliwiński ◽  
Paul Bartscht ◽  
Rocco Haase ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Healthy Controls ◽  
K Nearest Neighbor ◽  
Gait Patterns

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

scholarly journals Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2172 ◽  
Author(s):  
Andrea Tacchella ◽  
Silvia Romano ◽  
Michela Ferraldeschi ◽  
Marco Salvetti ◽  
Andrea Zaccaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Learning Algorithm ◽  
Collective Intelligence ◽  
Learning Algorithms ◽  
Predictive Ability ◽  
Machine Learning Algorithms ◽  
Or Groups ◽  
Clinical Records ◽  
Proof Of Principle

Background:Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options.Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated.Methods:Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome.Results:A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record.Conclusions:In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

scholarly journals Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2172 ◽  
Author(s):  
Andrea Tacchella ◽  
Silvia Romano ◽  
Michela Ferraldeschi ◽  
Marco Salvetti ◽  
Andrea Zaccaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Learning Algorithm ◽  
Collective Intelligence ◽  
Learning Algorithms ◽  
Predictive Ability ◽  
Machine Learning Algorithms ◽  
Or Groups ◽  
Clinical Records ◽  
Proof Of Principle

Background:Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options.Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated.Methods:Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome.Results:A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record.Conclusions:In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

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