Prediction of Dyslexia Using Machine Learning—A Research Travelogue

Prabha, A. Jothi; Bhargavi, R.

doi:10.1007/978-981-13-7091-5_3

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Skin disease 35 has become common among millions of people. Usually, skin diseases have many hidden dangers that lead to a lack of self-confidence and psychological depression.…”

Section: Prediction Of Skin Diseasementioning

confidence: 99%

A systematic review on Machine learning and Neural Network based models for disease prediction

Roobini,

Kavitha,

Karthik

2024

JIST

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Over the recent years, conventional artificial intelligence (AI) has witnessed a significant infusion of machine learning and neural networks, marking a substantial evolution in various domains due to their autonomous capacity for feature acquisition and remarkable efficiency. Particularly in the medical field, machine learning-based models have outperformed physicians, exhibiting greater accuracy. Diseases such as cancer, Alzheimer's, dyslexia, skin diseases, and heart diseases have become focal points in medical research. Several deep learning methods, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, Logistic Regression, Decision Tree, and Recurrent Neural Networks (RNN), play crucial roles in disease prediction. This survey emphasizes the critical analysis of which deep learning models achieve higher accuracy in predicting specific diseases. The objective is to shed light on existing shortcomings in disease prediction and propose potential remedies for future improvements. Results indicate that Convolutional Neural Networks excel in predicting heart and Alzheimer's diseases, as well as breast cancer. Support Vector Machines demonstrate effectiveness in cancer prediction, while logistic regression proves adept at predicting dyslexia, and decision trees emerge as a favorable choice for skin diseases. Looking ahead, the integration of digital twins for predictive analytics, facilitating the simulation and modeling of disease progression based on individual patient characteristics, and leveraging blockchain for secure storage and sharing of health data represent promising avenues for future developments.

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“…Skin disease 35 has become common among millions of people. Usually, skin diseases have many hidden dangers that lead to a lack of self-confidence and psychological depression.…”

Section: Prediction Of Skin Diseasementioning

confidence: 99%

A systematic review on Machine learning and Neural Network based models for disease prediction

Roobini,

Kavitha,

Karthik

2024

JIST

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“…The integration of artificial intelligence (AI)-based systems, particularly those employing deep learning approaches, has been progressive in the development of dyslexia diagnosis. Traditional approaches relied on manual evaluations, but the development of AI has permitted more efficient and subtle detection systems (Jothi Prabha and Bhargavi, 2019). Deep learning, a subset of AI, has been shown to be very effective in dyslexia-detection exercises (Poulsen et al, 2023).…”

Section: Deep Learning-based Detection Of Dyslexia Diseasementioning

confidence: 99%

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Aldehim,

Rashid,

Alluhaidan

et al. 2024

Journal of Disability Research

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Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

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“…Vanitha and Kasthuri [15] Review of machine learning algorithms used in Dyslexia prediction Chakraborty et al [16] presents a survey paper on the topic of machine learning algorithms for learning disability prediction. But the presentation was some what abstract level and had no details about dysgraphia diagnosis systems Vanjari et al [17] A review on learning disabilities and technologies determining the severity of learning disabilities Prabha and Bhargavi [18] A similar work like [15], a brief review on prediction of dyslexia using machine learning Saxena and Saxena [19] Reviews and explains the role of machine learning in learning disabilities diagnosis but lacks the insight into dysgraphia Jamhar et al [20] A systematic review about machine learning methods for learning disorder prediction. But not addressed about dysgraphia.…”

Section: Introductionmentioning

confidence: 99%

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Kunhoth,

Al-Maadeed,

Kunhoth

et al. 2024

IJDAR

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Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.

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Prediction of Dyslexia Using Machine Learning—A Research Travelogue

Cited by 9 publications

References 19 publications

A systematic review on Machine learning and Neural Network based models for disease prediction

A systematic review on Machine learning and Neural Network based models for disease prediction

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

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