The primary goal of this project is to build an intelligent prediction model that can help determine whether a patient is likely to have hypothyroidism based on key medical indicators. Hypothyroidism, a condition in which the thyroid gland underperforms, affects various bodily functions and can lead to serious health issues if not diagnosed early. Traditional methods of diagnosis often require extensive tests and clinical interpretation. This project addresses the challenge by applying the Support Vector Machine (SVM) algorithm, a widely used supervised machine learning technique, to identify patterns and classify patient data into hypothyroid or non-hypothyroid categories. By the end of the project, students will deliver a functioning prediction model that provides accurate insights while emphasizing that clinical validation is still essential.
The project unfolds across twelve weeks, starting with the installation of Python and relevant machine learning libraries, followed by the collection and preparation of raw or unlabeled medical datasets. Students will explore SVM concepts and apply them to design the core classification model using Scikit-learn and other Python-based libraries.
In the middle phase, they will train and validate the model using real or simulated patient data, iterating to improve performance. The testing phase will include evaluating accuracy, identifying misclassifications, and refining model parameters. The final stages will focus on documentation, creating a user interface if desired, and presenting a final group demonstration. Although the system will support diagnosis, it will not replace professional medical consultation and must be used as an assistive tool only.
