Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children

David, Julie M.; Balakrishnan, Kannan

doi:10.5120/1432-1931

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…The results obtained from this study are compared with the results of our other similar studies conducted based on J48, Naive Bayes, Support Vector Machines (SVM ) and Muliti Layer Perceptron classifiers [42,13]. The comparison of the results is shown in Table 15 below.…”

Section: Comparison Of Resultsmentioning

confidence: 77%

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Kannan¹,

Julie²

2013

IJISA

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Abstract-Learn ing Disability (LD) is a classification including several d isorders in wh ich a ch ild has difficulty in learning in a typical manner, usually caused by an unknown factor or factors . LD affects about 15% of children enrolled in schools. The prediction of learning disability is a comp licated task since the identification of LD fro m diverse features or signs is a co mplicated problem. There is no cure for learning disabilit ies and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some t ime. The aim of this paper is to develop a new algorith m for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the perfo rmance of fuzzy and neuro fu zzy classifiers with specific emphasis on prediction of learning d isabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorith m, viz. the correlation based new algorith m for imputing the missing values and Principal Co mponent Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in predict ion system and is capable of improving the performance of a classifier.

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Section: Comparison Of Resultsmentioning

confidence: 77%

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Kannan¹,

Julie²

2013

IJISA

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“…Such classifiers; will be used to implement the computerized proposed analytical model.Athanasios et al [9] stated that Artificial intelligence methods are used to discover special education needs learners to form dyslexia and to discover their excellence. Julie et al [10] developed a prediction model which mainly focuses on missing value issues and dimensionality reduction by validating using neuro-fuzzy classifier in the field of discovering learning disabilities among school children. Manghirmalani et al [11] introduced a learning vector quantization which classifies whether the children's have dyslexia or not using rule-based model and it also classifies them into types of learning disabilities.…”

Section: Related Workmentioning

confidence: 99%

Whale Behavior based Rule Optimization on Paraconsistent Neutrosophic Classification Model for Prediction of Dyslexia

Zeema¹,

Christopher²

2019

IJRTE

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With the advancement in the software field, diagnosing dyslexia in earlier stages among children is highly possible. It helps them to take necessary measures to rise above the problem. This paper intends to develop an uncertainty handling model using neutrosophic logic inference system. This system’s functionality is enhanced by introducing paraconsistent logic with whale behavior based optimization. Paraconsistent logic is used to discover the degree of certainty and contradiction of generated rules. Pruning the population of rules is handled by a nature inspire algorithm known as whale behavior based rule optimization. Dyslexia dataset consists of both vague and crisp values. Treating them as such will often lead to high false alarms in the detection process. To overcome this issue the neutrosophic model is used to denote them in terms of membership degree of truthiness, indeterminacy, and falsity. The paraconsistent analyzer works with the favorable and unfavorable degree of evidence of each rule to handle the inconsistency and uncertainty among dyslexia detection. The potential rules are selected by the encircle prey model of the whale optimization algorithm. The simulation results proved that the performance of the proposed model produces high detection rate in the detection of dyslexia.

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“…The study [21] analyzed the association between language, motor, social, and cognitive development from identified diseases, visual problems, psychological and intellectual development, other diseases, and types of delay and, using compositions of the decision tree, made 14 association rules derived scores support and confidence scores. David and Balakrishnan [22] applied a decision tree algorithm and rough sets for the prediction of learning disabilities in school-age children using a checklist of 16 most frequent signs and symptoms of learning disabilities (n=513, area under the receiver operating characteristic curve [AUROC] 0.985). Varol et al [23] present the application of machine learning methods for early prediction of reading disability, collecting 356 samples using 40 features, including demographics, pretesting, and weekly monitoring (word identification fluency); the comparison was made using 6 classification algorithms, and the best result was an AUROC of 0.942.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Health Insurance Big Data for Early Detection of Disabilities: Algorithm Development and Validation

et al. 2020

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Background Early detection of childhood developmental delays is very important for the treatment of disabilities. Objective To investigate the possibility of detecting childhood developmental delays leading to disabilities before clinical registration by analyzing big data from a health insurance database. Methods In this study, the data from children, individuals aged up to 13 years (n=2412), from the Sample Cohort 2.0 DB of the Korea National Health Insurance Service were organized by age range. Using 6 categories (having no disability, having a physical disability, having a brain lesion, having a visual impairment, having a hearing impairment, and having other conditions), features were selected in the order of importance with a tree-based model. We used multiple classification algorithms to find the best model for each age range. The earliest age range with clinically significant performance showed the age at which conditions can be detected early. Results The disability detection model showed that it was possible to detect disabilities with significant accuracy even at the age of 4 years, about a year earlier than the mean diagnostic age of 4.99 years. Conclusions Using big data analysis, we discovered the possibility of detecting disabilities earlier than clinical diagnoses, which would allow us to take appropriate action to prevent disabilities.

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Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children

Cited by 18 publications

References 16 publications

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Whale Behavior based Rule Optimization on Paraconsistent Neutrosophic Classification Model for Prediction of Dyslexia

Analysis of Health Insurance Big Data for Early Detection of Disabilities: Algorithm Development and Validation

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