Engineering Student CGPA group classification by Machine Learning

Machine learning has been a trending topic for which almost every research area would like to incorporate some of the technique in their studies. In this paper, we demonstrate several machine learning models using two different data sets. One data set is the thermograms time series data on a cancer study that was conducted at the University of Louisville Hospital, and the other set is from the world-renowned Framingham Heart Study. Thermograms can be used to determine a patient’s health status, yet the difficulty of analyzing such a high-dimensional dataset makes it rarely applied, especially in cancer research. Previously, Rai et al.1 proposed an approach for data reduction along with comparison between parametric method, non-parametric method (KNN), and semiparametric method (DTW-KNN) for group classification. They concluded that the performance of two-group classification is better than the three-group classification. In addition, the classifications between types of cancer are somewhat challenging. The Framingham Heart Study is a famous longitudinal dataset which includes risk factors that could potentially lead to the heart disease. Previously, Weng et al.2 and Alaa et al.3 concluded that machine learning could significantly improve the accuracy of cardiovascular risk prediction. Since the original Framingham data have been thoroughly analyzed, it would be interesting to see how machine learning models could improve prediction. In this manuscript, we further analyze both the thermogram and the Framingham Heart Study datasets with several learning models such as gradient boosting, neural network, and random forest by using SAS Visual Data Mining and Machine Learning on SAS Viya. Each method is briefly discussed along with a model comparison. Based on the Youden’s index and misclassification rate, we select the best learning model. For big data inference, SAS Visual Data Mining and Machine Learning on SAS Viya, a cloud computing and structured statistical solution, may become a choice of computing.

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Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Scientific Reports ◽

10.1038/s41598-020-77474-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yuta Suzuki ◽

Hideitsu Hino ◽

Takafumi Hawai ◽

Kotaro Saito ◽

Masato Kotsugi ◽

...

Keyword(s):

Machine Learning ◽

Crystal Structure Analysis ◽

Group Classification ◽

X Ray Diffraction ◽

Space Group ◽

X Ray ◽

Crystal System ◽

Machine Learning Approach ◽

Diffraction Patterns ◽

Xrd Patterns

AbstractDetermination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.

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Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer’s Disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617720000144 ◽

2020 ◽

Vol 26 (7) ◽

pp. 690-700

Author(s):

Russell Binaco ◽

Nicholas Calzaretto ◽

Jacob Epifano ◽

Sean McGuire ◽

Muhammad Umer ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Group Classification ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

AbstractObjective:To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).Methods:dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.Results:Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.Conclusion:Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

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Investigating the power of eyes open resting state EEG for assisting in dementia diagnosisComputing

10.21203/rs.3.rs-245206/v1 ◽

2021 ◽

Author(s):

Jack Jennings ◽

Luis R Peraza ◽

Mark Baker ◽

Kai Alter ◽

John-Paul Taylor ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Classification Accuracy ◽

Dominant Frequency ◽

Group Classification ◽

Support Vector ◽

Potential Candidate ◽

Healthy Controls ◽

Nearest Neighbour ◽

Eyes Open

Abstract Introduction: The differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability.Methods: We extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer’s disease (AD), Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) respectively and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands. Results: We found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs Dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k nearest neighbour machine learning model which outperformed other machine learning methods. Conclusions: The findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.

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Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping

Current Alzheimer Research ◽

10.2174/1567205017666200624204427 ◽

2020 ◽

Vol 17 (5) ◽

pp. 428-437

Author(s):

Hyug-Gi Kim ◽

Soonchan Park ◽

Hak Y. Rhee ◽

Kyung M. Lee ◽

Chang-Woo Ryu ◽

...

Keyword(s):

Machine Learning ◽

Entorhinal Cortex ◽

Clinical Data ◽

Gray Matter ◽

Gray Matter Volume ◽

Posterior Cingulate Cortex ◽

Group Classification ◽

Svm Classifier ◽

Quantitative Susceptibility Mapping ◽

Matter Volume

Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

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Machine Learning Methodology for the Analysis of Engineering Student Retention Data

Proceedings of the Canadian Engineering Education Association (CEEA) ◽

10.24908/pceea.v0i0.4812 ◽

2013 ◽

Author(s):

Thomas E. Doyle ◽

Spencer Smith ◽

Philip Gabardo

Keyword(s):

Machine Learning ◽

Student Retention ◽

Retention Data ◽

Engineering Student

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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