Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

AbstractAlong tract statistics enables white matter characterization using various diffusion MRI (dMRI) metrics. Here, we applied a machine learning (ML) method to assess the clinical utility of dMRI metrics along corticospinal tracts (CST), investigating whether motor glioma patients can be classified with respect to their motor status. The ML-based analysis included developing models based on support vector machine (SVM) using histogram-based measures of dMRI-based tract profiles (e.g., mean, standard deviation, kurtosis and skewness), following a recursive feature elimination (RFE) method based on SVM (SVM-RFE). Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% AUC). Incorporating the patients’ demographics and clinical features such as age, tumor WHO grade, tumor location, gender and resting motor threshold (RMT) into our designed models demonstrated that these features were not as effective as microstructural measures. The results revealed that ADC, FA and RD contributed more than other features to the model.

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Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽

10.3390/diagnostics11030574 ◽

2021 ◽

Vol 11 (3) ◽

pp. 574

Author(s):

Gennaro Tartarisco ◽

Giovanni Cicceri ◽

Davide Di Pietro ◽

Elisa Leonardi ◽

Stefania Aiello ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Behavioral Science ◽

Autistic Traits ◽

Classification Performance ◽

Recursive Feature Elimination ◽

Diagnostic Tools ◽

Support Vector ◽

K Nearest Neighbors ◽

Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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Predicting 30-Day Mortality after an Acute Coronary Syndrome (ACS) using Machine Learning Methods for Feature Selection, Classification and Visualisation

Sains Malaysiana ◽

10.17576/jsm-2021-5003-17 ◽

2021 ◽

Vol 50 (3) ◽

pp. 753-768

Author(s):

NANYONGA AZIIDA ◽

SORAYYA MALEK ◽

FIRDAUS AZIZ ◽

KHAIRUL SHAFIQ IBRAHIM ◽

SAZZLI KASIM

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Feature Selection ◽

High Performance ◽

Feature Selection Method ◽

Mortality Prediction ◽

Recursive Feature Elimination ◽

Support Vector ◽

Related Factors ◽

Coronary Syndrome

Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TIMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post-ACS. The performance of MLmodels using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with TIMI using an additional dataset resulted in the best ML model outperforming the TIMI score (AUC = 0.75 vs. AUC = 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator.

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Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

10.21203/rs.3.rs-40334/v1 ◽

2020 ◽

Author(s):

Gennaro Tartarisco ◽

Giovanni Cicceri ◽

Davide Di Pietro ◽

Stefania Aiello ◽

Elisa Leonardi ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

High Performance ◽

Autistic Traits ◽

Classification Performance ◽

Recursive Feature Elimination ◽

Diagnostic Tools ◽

Support Vector ◽

Behavioural Science ◽

Autism Screening

Abstract Background: In the past two decades, several screening instruments have been developed to detect toddlers who may be autistic, both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q−CHAT) is a quantitative and normally distributed measure of autistic traits which demonstrated good psychometric properties in different settings and cultures. Recently machine learning (ML) has been applied to behavioural science to improve classification performance of autism screening and diagnostic tools, but mainly in children, adolescents and adults. Methods: In this study, we used machine learning (ML) to investigate the accuracy and reliability of the Q−CHAT in discriminating young autistic children from those without. Three different ML algorithms (Random Forest, Naive Bayes and Support Vector Machine) were applied to investigate the complete set of Q-CHAT items and the best predictive items. Results: Our results showed that the three selected models outperformed the classical statistical methods of predictive validity and among the three ML classifiers, the Support Vector Machine was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the Support Vector Machine-Recursive Feature Elimination approach we were able to select a subset of 14 items ensuring an accuracy of 93%, while an accuracy of 83% was obtained from the best 3 discriminating items in common to our and the previous reported Q-CHAT-10. Limitations: Further data collection is needed.Conclusions: This evidence confirms the high performance and cross-cultural validity of the Q-CHAT and supports the application of ML to create shorter and faster versions of the instrument maintaining high classification accuracy, to be used as a quick, easy and high-performance tool in primary care settings.

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Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.798962 ◽

2021 ◽

Vol 4 ◽

Author(s):

Fan Zhang ◽

Melissa Petersen ◽

Leigh Johnson ◽

James Hall ◽

Sid E. O’Bryant

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

High Performance ◽

Support Vector ◽

Massive Datasets ◽

Complexity Of Algorithms ◽

Magnetic Resonance Imaging Mri ◽

Research Challenge ◽

Improved Accuracy

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

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NIMG-63. MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS

Neuro-Oncology ◽

10.1093/neuonc/noab196.561 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi143-vi144

Author(s):

Omaditya Khanna ◽

Anahita Fathi Kazerooni ◽

Jose A Garcia ◽

Chiharu Sako ◽

Sherjeel Arif ◽

...

Keyword(s):

Machine Learning ◽

Skull Base ◽

Predictive Model ◽

Patient Treatment ◽

Support Vector ◽

Benign Tumors ◽

Replication Cohort ◽

Discovery Cohort ◽

Ki 67 ◽

Who Grade

Abstract PURPOSE Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 < 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model. RESULTS A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively. CONCLUSION The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.

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Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports

JCO Clinical Cancer Informatics ◽

10.1200/cci.20.00028 ◽

2020 ◽

pp. 865-874

Author(s):

Enrico Santus ◽

Tal Schuster ◽

Amir M. Tahmasebi ◽

Clara Li ◽

Adam Yala ◽

...

Keyword(s):

Machine Learning ◽

Hybrid Systems ◽

High Performance ◽

Feature Model ◽

Training Data ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Extreme Gradient Boosting ◽

Pathology Reports

PURPOSE Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations. METHODS We collected 501 prostate pathology reports from 6 American hospitals. Reports were split into 2,711 core segments, annotated with 20 attributes describing the histology, grade, extension, and location of tumors. The data set was split by institutions to generate a cross-institutional evaluation setting. We assessed 4 systems, namely a rule-based approach, an ML model, and 2 hybrid systems integrating the previous methods: a Rule as Feature model and a Classifier Confidence model. Several ML algorithms were tested, including logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGB). RESULTS When training on data from a single institution, LR lags behind the rules by 3.5% (F1 score: 92.2% v 95.7%). Hybrid models, instead, obtain competitive results, with Classifier Confidence outperforming the rules by +0.5% (96.2%). When a larger amount of data from multiple institutions is used, LR improves by +1.5% over the rules (97.2%), whereas hybrid systems obtain +2.2% for Rule as Feature (97.7%) and +2.6% for Classifier Confidence (98.3%). Replacing LR with SVM or XGB yielded similar performance gains. CONCLUSION We developed methods to use pre-existing handcrafted rules to inform ML algorithms. These hybrid systems obtain better performance than either rules or ML models alone, even when training data are limited.

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Predicting failures of Molteno and Baerveldt glaucoma drainage devices using machine learning models

10.1101/646885 ◽

2019 ◽

Author(s):

Paul Morrison ◽

Maxwell Dixon ◽

Arsham Sheybani ◽

Bahareh Rahmani

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Intraocular Pressure ◽

Glaucoma Drainage Device ◽

Recursive Feature Elimination ◽

Support Vector ◽

Learning Models ◽

Glaucoma Drainage Devices ◽

Pressure Lowering ◽

Machine Learning Models

AbstractThe purpose of this retrospective study is to measure machine learning models’ ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient’s race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression.

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Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽

10.14704/web/v18si04/web18284 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 591-606

Author(s):

R. Brindha ◽

Dr.M. Thillaikarasi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Machine Learning Algorithms ◽

Geographical Information ◽

Recursive Feature Elimination ◽

Support Vector ◽

Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

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Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation (Preprint)

10.2196/preprints.18134 ◽

2020 ◽

Author(s):

Xiaodong Ding ◽

Feng Cheng ◽

Robert Morris ◽

Cong Chen ◽

Yiqin Wang

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Radial Artery ◽

Pulse Wave ◽

Disease Diagnosis ◽

Recursive Feature Elimination ◽

Physiological Parameter ◽

Support Vector ◽

External Interference ◽

Pulse Waves

BACKGROUND The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. OBJECTIVE The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. METHODS Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. RESULTS It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. CONCLUSIONS We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

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Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation

JMIR Medical Informatics ◽

10.2196/18134 ◽

2020 ◽

Vol 8 (6) ◽

pp. e18134

Author(s):

Xiaodong Ding ◽

Feng Cheng ◽

Robert Morris ◽

Cong Chen ◽

Yiqin Wang

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Radial Artery ◽

Pulse Wave ◽

Disease Diagnosis ◽

Recursive Feature Elimination ◽

Physiological Parameter ◽

Support Vector ◽

External Interference ◽

Pulse Waves

Background The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. Objective The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. Methods Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. Results It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. Conclusions We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

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