Thyroid Disease Prediction Leveraging Machine Learning for Early Recurrence Detection
Objective
The objective of this project is to develop an advanced machine learning-based system that accurately predicts thyroid diseases (Hyperthyroidism, Hypothyroidism, and Negative) and assesses the risk of disease recurrence. By leveraging algorithms such as SVM, AdaBoost, XGBoost, Decision Tree, and CNN, the system aims to facilitate early detection and prediction of recurrence based on critical clinical features like TSH levels, FT4, T3, and medical history. The goal is to provide healthcare providers with a reliable, user-friendly tool to make informed decisions, enabling timely interventions and personalized treatment plans that ultimately improve patient health outcomes and reduce long-term risks.
Abstract
Thyroid
diseases, including hyperthyroidism and hypothyroidism, are widespread and can
lead to serious complications if not detected early. Timely and accurate
prediction of thyroid conditions and the risk of disease recurrence can
significantly improve patient management and health outcomes. This project,
titled "Thyroid Disease Prediction Leveraging Machine Learning for Early
Recurrence Detection," applies cutting-edge machine learning techniques to
predict thyroid conditions and forecast the risk of recurrence based on patient
health data.
Keywords:
Thyroid Disease, Hyperthyroidism, Hypothyroidism, Machine Learning, Disease Recurrence, Recurrence Prediction, Early Detection, SVM, AdaBoost, XGBoost, Decision Tree, CNN, Predictive Modeling, Healthcare Analytics, Clinical Prediction, Medical Diagnosis.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE REQUIREMENTS
• Processor - I5/Intel Processor
• RAM - 8GB (min)
• Hard Disk - 160 GB
• Key Board - Standard Windows Keyboard
• Mouse - Two or Three Button Mouse
• Monitor - Any
SOFTWARE REQUIREMENS
• Operating System : Windows 7/8/10
• Server side Script : HTML, CSS, Bootstrap & JS
• Programming Language : Python
• Libraries : Flask, Pandas, Mysql.connector, Os, Numpy,
Scikit-learn.
• IDE/Workbench : VS-Code
• Technology : Python 3.10+
• Server Deployment : Xampp Server
• Database : MySQL
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