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Article ; Online: Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder

Mousumi Bala / Mohammad Hanif Ali / Md. Shahriare Satu / Khondokar Fida Hasan / Mohammad Ali Moni

Algorithms, Vol 15, Iss 166, p

2022 Volume 166

Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can ... ...

Abstract	Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. Using various artificial intelligence (AI) techniques, ASD can be detected an at earlier stage than with traditional methods. The aim of this study was to propose a machine learning model that investigates ASD data of different age levels and to identify ASD more accurately. In this work, we gathered ASD datasets of toddlers, children, adolescents, and adults and used several feature selection techniques. Then, different classifiers were applied into these datasets, and we assessed their performance with evaluation metrics including predictive accuracy, kappa statistics, the f1-measure, and AUROC. In addition, we analyzed the performance of individual classifiers using a non-parametric statistical significant test. For the toddler, child, adolescent, and adult datasets, we found that Support Vector Machine (SVM) performed better than other classifiers where we gained 97.82% accuracy for the RIPPER-based toddler subset; 99.61% accuracy for the Correlation-based feature selection (CFS) and Boruta CFS intersect (BIC) method-based child subset; 95.87% accuracy for the Boruta-based adolescent subset; and 96.82% accuracy for the CFS-based adult subset. Then, we applied the Shapley Additive Explanations (SHAP) method into different feature subsets, which gained the highest accuracy and ranked their features based on the analysis.
Keywords	ASD ; machine learning ; classifier ; feature selection ; prediction model ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine Learning-Based Approaches for Forecasting COVID-19 Cases in Bangladesh

Satu, Md. Shahriare / Howlader, Koushik Chandra / Islam, Sheikh Mohammed Shariful

SSRN Electronic Journal ; ISSN 1556-5068

2020

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
DOI	10.2139/ssrn.3614675
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.

Howlader, Koushik Chandra / Satu, Md Shahriare / Awal, Md Abdul / Islam, Md Rabiul / Islam, Sheikh Mohammed Shariful / Quinn, Julian M W / Moni, Mohammad Ali

Health information science and systems

2022 Volume 10, Issue 1, Page(s) 2

Abstract: Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish ... ...

Abstract	Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population. Supplementary information: The online version contains supplementary material available at 10.1007/s13755-021-00168-2.
Language	English
Publishing date	2022-02-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2697647-X
ISSN	2047-2501
ISSN	2047-2501
DOI	10.1007/s13755-021-00168-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.

Satu, Md Shahriare / Khan, Md Imran / Rahman, Md Rezanur / Howlader, Koushik Chandra / Roy, Shatabdi / Roy, Shuvo Saha / Quinn, Julian M W / Moni, Mohammad Ali

Briefings in bioinformatics

2021 Volume 22, Issue 2, Page(s) 1415–1429

Abstract: With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and ... ...

Abstract	With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.
MeSH term(s)	COVID-19/complications ; COVID-19/virology ; Gene Ontology ; Humans ; Neoplasms/complications ; SARS-CoV-2/isolation & purification ; Signal Transduction ; Transcriptome
Language	English
Publishing date	2021-02-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab003
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

Satu, Md Shahriare / Khan, Md Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M W / Moni, Mohammad Ali

Knowledge-based systems

2021 Volume 226, Page(s) 107126

Abstract: COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic ... ...

Abstract	COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.
Language	English
Publishing date	2021-05-06
Publishing country	Netherlands
Document type	Journal Article
ISSN	0950-7051
ISSN	0950-7051
DOI	10.1016/j.knosys.2021.107126
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.

Akter, Tania / Ali, Mohammad Hanif / Khan, Md Imran / Satu, Md Shahriare / Uddin, Md Jamal / Alyami, Salem A / Ali, Sarwar / Azad, Akm / Moni, Mohammad Ali

Brain sciences

2021 Volume 11, Issue 6

Abstract: Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan ... ...

Abstract	Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.
Language	English
Publishing date	2021-05-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci11060734
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Article ; Online: Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

Ahammed, Khair / Satu, Md. Shahriare / Abedin, Mohammad Zoynul / Rahaman, Md. Auhidur / Islam, Shiekh Mohammed Shariful

medRxiv

Abstract: This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using ... ...

Abstract	This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.
Keywords	covid19
Language	English
Publishing date	2020-06-08
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.06.07.20124594
Database	COVID19

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Article ; Online: Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Md. Shahriare Satu / Koushik Chandra Howlader / Mufti Mahmud / M. Shamim Kaiser / Sheikh Mohammad Shariful Islam / Julian M. W. Quinn / Salem A. Alyami / Mohammad Ali Moni

Applied Sciences, Vol 11, Iss 4266, p

2021 Volume 4266

Abstract: The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health ... ...

Abstract	The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.
Keywords	COVID-19 ; machine learning ; infected cases ; forecasting ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment inCOVID-19 Tweets

Satu, Md. Shahriare / Khan, Md. Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M. W. / Moni, Mohammad Ali

Abstract: COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected ... ...

Abstract	COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.
Keywords	covid19
Publisher	MedRxiv; WHO
Document type	Article ; Online
Note	WHO #Covidence: #20167973
DOI	10.1101/2020.08.04.20167973
Database	COVID19

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Article ; Online: TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment in COVID-19 Tweets

Satu, Md. Shahriare / Khan, Md. Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M. W. / Moni, Mohammad Ali

medRxiv

Abstract	COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.
Keywords	covid19
Language	English
Publishing date	2020-08-04
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.08.04.20167973
Database	COVID19

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