Intelligent Frame Work to Predict Autism in Infants using Machine Learning

Autistic Spectrum Disorder (ASD) is a behavioral impairment that interferes with the use of auditory, communicative, cognitive, abilities and social skills. ADS was introduce researched using advanced approaches based on machine learning to speed up the diagnostic period or increase the responsiveness, reliability or precision of the diagnosis process. Normal medical checkup data (training data) of the baby, test data is used to classify the child with autism. The assessment results showed that, for both types of datasets, the proposed prediction model produces better results in terms of precision, responsiveness, precision and false positive rate (FPR). In the modern day, Autism Spectrum Disorder (ASD) is gaining some momentum quicker than ever. Detecting signs of autism by screening tests is very costly and time consuming. Autism can be identified faster by combining artificial intelligence and Machine Learning (ML)

Download Full-text

Identification of newborns at risk for autism using electronic medical records and machine learning

10.1101/19008367 ◽

2019 ◽

Author(s):

Rayees Rahman ◽

Arad Kodesh ◽

Stephen Z Levine ◽

Sven Sandin ◽

Abraham Reichenberg ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Positive Predictive Value ◽

Electronic Medical Records ◽

Predictive Value ◽

False Positive ◽

Medical Records ◽

False Positive Rate ◽

Autism Spectrum ◽

Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

Download Full-text

Identification of newborns at risk for autism using electronic medical records and machine learning

European Psychiatry ◽

10.1192/j.eurpsy.2020.17 ◽

2020 ◽

Vol 63 (1) ◽

Author(s):

Rayees Rahman ◽

Arad Kodesh ◽

Stephen Z. Levine ◽

Sven Sandin ◽

Abraham Reichenberg ◽

...

Keyword(s):

Machine Learning ◽

General Population ◽

Electronic Medical Records ◽

False Positive ◽

Medical Records ◽

False Positive Rate ◽

Autism Spectrum ◽

Health Maintenance ◽

C Statistic ◽

Positive Rate

Abstract Background. Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. Methods. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). Results. All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. Conclusions. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

Download Full-text

Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Computers ◽

10.3390/computers8020035 ◽

2019 ◽

Vol 8 (2) ◽

pp. 35 ◽

Cited By ~ 2

Author(s):

Xuan Dau Hoang ◽

Ngoc Tuong Nguyen

Keyword(s):

Machine Learning ◽

Web Applications ◽

False Positive Rate ◽

Training Data ◽

Machine Learning Techniques ◽

Web Pages ◽

Government Organizations ◽

Detection Model ◽

Learning Techniques ◽

Positive Rate

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.

Download Full-text

A Machine Learning Way to Classify Autism Spectrum Disorder

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v16i06.19559 ◽

2021 ◽

Vol 16 (06) ◽

pp. 182

Author(s):

Sujatha R ◽

Aarthy SL ◽

Jyotir Moy Chatterjee ◽

A. Alaboudi ◽

NZ Jhanjhi

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Stochastic Gradient Descent ◽

Screening Tests ◽

Support Vector ◽

Attention Switching ◽

Adaptive Boosting ◽

Over The Top

In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.

Download Full-text

A Case Study: Unique Challenges of an Adolescent Female With Autism Spectrum Disorder

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_persp-19-00132 ◽

2020 ◽

Vol 5 (1) ◽

pp. 314-325

Author(s):

Kimberly F. Frazier ◽

Jessica Collier ◽

Rachel Glade

Keyword(s):

Autism Spectrum Disorder ◽

Cognitive Abilities ◽

Social Cognitive ◽

Social Performance ◽

Management Strategies ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Self Management ◽

Adolescent Female ◽

Social Thinking

Background The aim of this study was to determine the clinical efficacy of combining self-management strategies and a social thinking approach to address the social performance and executive function of an adolescent female with autism spectrum disorder. Method This research examined the effects of a social knowledge training program, “Think Social,” as well as strategies to improve higher order cognitive abilities. Results and Conclusion Although quantitative improvement was not found, several qualitative gains in behavior were noted for the participants of this study, suggesting a benefit from using structured environmental cues of self-management strategies, as well as improved social understanding through social cognitive training.

Download Full-text

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Download Full-text

Screening for Lead Poisoning in an Urban Pediatric Clinic Using Samples Obtained by Fingerstick

PEDIATRICS ◽

10.1542/peds.94.2.174 ◽

1994 ◽

Vol 94 (2) ◽

pp. 174-179

Author(s):

David J. Schonfeld ◽

Mark R. Cullen ◽

Petrie M. Rainey ◽

Anne T. Berg ◽

David R. Brown ◽

...

Keyword(s):

Lead Poisoning ◽

False Positive ◽

Lead Concentration ◽

Capillary Tube ◽

Graphite Furnace ◽

False Positive Rate ◽

Screening Tests ◽

Zinc Protoporphyrin ◽

Pediatric Clinic ◽

Positive Rate

Objective. To assess the false positive rate of blood (BPb) determinations on sample obtained by fingerstick from children screened in an urban clinic. Method. From a single fingerstick (N = 1573), blood was collected in a capillary tube for determining lead concentration (CPb) by graphite furnace and an additional sample was absorbed onto a filter paper for determining lead concentration (FPb) by atomic absorption spectrophotometry with Delves cup. Zinc protoporphyrin (ZPP) was measured immediately and a confirmatory venous lead (VPb) specimen was obtained at the same visit if the ZPP was ≥35 µg/dL (0.6 µmol/L); children with either a CPb or FPb ≥15 µg/dL (0.7 µmol/L) were later recalled for determining VPb. Results. For the 172 children who had a VPb on the same day as the screening tests, the false positive rates (95% confidence intervals) at a lead threshold of 15 µg/dL (0.7 µmol/L) were: CPb, 13.5% (6.7-20.3); FPb, 19.1% (11.8-26.4). Analyses using all 679 screens with a paired venous specimen (mean delay between screen and venous testing = 30 days) yielded much higher false positive rates (CPb, 31.3%; FPb, 46.0%). Conclusions. Screening for lead poisoning is feasible within an urban pediatric clinic by direct measurement of lead concentration in blood samples obtained by fingerstick. The false positive rate that can be obtained is acceptable given the precision of measuring BPb concentration. Practitioners using a staged screening protocol may incorrectly attribute a higher false positive rate to the screening tests, when much of the error may be due to the temporal variability of BPb resulting from both biologic variability in BPb concentration and intermittent exposures.

Download Full-text

Utilizing two-tiered screening for early detection of autism spectrum disorder

Autism ◽

10.1177/1362361317712649 ◽

2017 ◽

Vol 22 (7) ◽

pp. 881-890 ◽

Cited By ~ 11

Author(s):

Meena Khowaja ◽

Diana L Robins ◽

Lauren B Adamson

Keyword(s):

Early Detection ◽

Young Children ◽

False Positive ◽

Screening Tool ◽

False Positive Rate ◽

Autism Spectrum ◽

Screening Methods ◽

Positive Rate ◽

Level 1 ◽

Level 2

Despite advances in autism screening practices, challenges persist, including barriers to implementing universal screening in primary care and difficulty accessing services. The high false positive rate of Level 1 screening methods presents especially daunting difficulties because it increases the need for comprehensive autism evaluations. This study explored whether two-tiered screening—combining Level 1 (Modified Checklist for Autism in Toddlers, Revised with Follow-Up) and Level 2 (Screening Tool for Autism in Toddlers and Young Children) measures—improves the early detection of autism. This study examined a sample of 109 toddlers who screened positive on Level 1 screening and completed a Level 2 screening measure prior to a diagnostic evaluation. Results indicated that two-tiered screening reduced the false positive rate using published Screening Tool for Autism in Toddlers and Young Children cutoffs compared to Level 1 screening alone, although at a cost to sensitivity. However, alternative Screening Tool for Autism in Toddlers and Young Children scoring in the two-tiered screening improved both positive predictive value and sensitivity. Exploratory analyses were conducted, including comparison of autism symptoms and clinical profiles across screening subsamples. Recommendations regarding clinical implications of two-tiered screening and future areas of research are presented.

Download Full-text

Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

10.2196/preprints.24754 ◽

2020 ◽

Author(s):

Haishuai Wang ◽

Paul Avillach

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Deep Learning ◽

The United States ◽

Autism Spectrum ◽

Repetitive Behaviors ◽

Spectrum Disorder ◽

Screening Tools ◽

Common Variants ◽

Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

Download Full-text