ThyroCare: A Mobile Application Thyroid Diseases Classification Using Supervised Machine Learning Algorithms
The ThyroCare mobile application addresses major pain points users encounter when managing their thyroid health through key product features such as blood test, medication management and medicine reminder.
The thyroid plays a crucial role in the body by producing and managing thyroid hormones that regulate metabolism. A few distinct hormones produced by the thyroid, including T4 (thyroxine, which includes four iodide atoms), and T3 (triiodothyronine, contains three iodide atoms), regulate metabolism. Compared to men, women have a diagnosis of thyroid disease at a rate of five to eight times higher.
Since machine learning algorithms play a significant role in classifying thyroid diseases, they are of great help with predicting and identifying many diseases, such as thyroid diseases. Deploying this machine learning algorithm into a mobile application surely helps those who are in question of their own health conditions and hopefully give awareness of such disease.
The Project Design “ThyroCare: A Mobile Application Thyroid Diseases Classification Using Supervised Machine Learning Algorithms” — aimed the following:
- Generate a thyroid disease dataset from existing data using synthetic data generation.
- Determine the performance of each machine learning classifier model in terms of classifying thyroid diseases.
- Incorporate a working Android mobile application that processes and analyzes user data with the final machine learning model.
This study explores how supervised machine learning algorithms can be applied to discover such thyroid disorders found in the body. The researchers will be collecting data from existing data sets available and with that information the researchers will train and create a model of the selected machine learning algorithm with the highest accuracy and performance that will be extracted and further developed in the mobile application.
Although thyroid diseases come with a wide range form, this study focuses specifically on identifying the most common thyroid diseases which include hyperthyroidism and hypothyroidism.
