A Comparative Classification Models Study for Development of Early Dyslexia Screening System
| Autor: | Ng Li Mun, Nur Anida Jumadi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Dyslexia Fuzzy control system medicine.disease Machine learning computer.software_genre Fuzzy logic Education Random forest Naive Bayes classifier Statistical classification Robustness (computer science) medicine Artificial intelligence Decision table business computer |
| Zdroj: | Universal Journal of Educational Research. 8:1-15 |
| ISSN: | 2332-3213 2332-3205 |
| DOI: | 10.13189/ujer.2020.081501 |
| Popis: | This paper presents the development of a rapid dyslexia screening system using Fuzzy Inference System (FIS) and comparative study using WEKA analysis (Random Forest, Decision Table and Naive Bayes). The developed fuzzy system is able to output two risk conditions namely as High Risk and Low Risk of dyslexia based on the defined rule statements. The system performance is evaluated using pre-existed data (n = 30), which is comprised of dyslexia and slow learner subjects. The proposed fuzzy system achieves overall accuracy of 56.7 % (n = 30) whereas the accuracy of the system towards dyslexia subjects is 100 % (n = 17). The low percentage in overall accuracy is due to insufficient tuning of the defined rule statements when analysing extreme conditions related to slow learners. On the other hand, the best classification algorithms are Decision Table (73.33 %) and Random Forest (82.35 %) when using both subjects groups and dyslexia subjects, respectively. A larger dataset is needed to achieve better accuracy when conducting data mining. Therefore, modification of rule statements and additional IQ test will be added in the future in order to improve the accuracy and robustness of the fuzzy inference system towards identifying slow learner from dyslexia. |
| Databáze: | OpenAIRE |
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