Dyslexia Prediction Flask App Prediction in Python Projects

Dyslexia Prediction Flask App Prediction in Python Projects
Abstract
The project “Dyslexia Prediction Flask App in Python” is designed to assist in the early detection of dyslexia using machine learning techniques. Dyslexia is a learning disorder that affects reading, writing, and comprehension skills, and early prediction can help in timely intervention. The system collects input data such as reading test scores, spelling errors, phonological awareness, memory patterns, and behavioral assessments, which are then processed and classified using trained machine learning models. Python libraries like Scikit-learn, Pandas, NumPy, and TensorFlow/Keras are employed to train and evaluate classifiers such as Decision Trees, Random Forest, SVM, or Neural Networks. The model is integrated into a Flask web application, allowing users (teachers, parents, or healthcare professionals) to enter test data and receive predictions on whether a student is at risk of dyslexia. This solution provides an accessible, user-friendly tool for supporting educational and clinical diagnosis.

Existing System
Current dyslexia diagnosis is mostly carried out through manual psychological and educational assessments, involving reading and writing tests administered by experts. While accurate, these methods are time-consuming, require professional intervention, and are often delayed until symptoms become severe. Some computer-based tools exist, but they are either proprietary, expensive, or lack intelligent predictive models. Moreover, existing systems often fail to provide real-time, web-based tools that educators and parents can easily access for early screening purposes.

Proposed System

The proposed system introduces a machine learning-based dyslexia prediction model integrated into a Flask application. The pipeline begins with data preprocessing, including feature scaling, normalization, and handling missing values. Machine learning models are trained on dyslexia-related datasets to classify students as dyslexic or non-dyslexic, with probability scores to indicate prediction confidence. The Flask app provides a web interface where users can input student details, reading performance, and test scores to obtain instant predictions. The system can also maintain prediction history, visualize results through charts, and export reports for further evaluation. This approach not only makes dyslexia prediction faster and more accessible but also supports teachers and clinicians in making data-driven decisions for intervention strategies.

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