Thyroid Disease Prediction 🧬🩺

A machine learning project developed to predict the recurrence of well-differentiated thyroid cancer using patient clinical and pathological data. This project aims to assist healthcare professionals in making data-driven decisions through predictive analytics.

📊 Project Overview

This healthcare analytics project leverages classification models to identify high-risk patients using 15 years of retrospective data. It includes:

• End-to-end pipeline from EDA, preprocessing, modeling, to deployment
• Interactive Power BI dashboard
• Ready-to-use model prediction interface

🔍 Key Features

• Exploratory Data Analysis (EDA) on demographics, tumor characteristics, and risk factors

• Data preprocessing: missing value handling, encoding categorical features, and scaling numerical features

• Model building using:

  • Logistic Regression
  • Support Vector Classifier
  • Random Forest (Best Model: 98.7% Accuracy)
  • Gradient Boosting, Bagging, and SGD Classifier

• Hyperparameter tuning for Random Forest

• Model deployment: save/load with joblib, single prediction interface

• Power BI Dashboard for visual analytics

📈 Model Performance

Model	Accuracy
Logistic Regression	97.4%
SGD Classifier	94.8%
Support Vector Classifier (SVC)	96.1%
Random Forest (Best)	98.7%
Gradient Boosting	97.4%
Bagging Classifier	97.4%

• Best Model: Random Forest
• Accuracy: 98.7%
• Precision/Recall/F1: High across all metrics
• Top Predictive Features: Stage, Pathology Type, Age, Focality, Radiotherapy History

📊 Power BI Dashboard (Visual Analytics)

This project includes a Power BI dashboard that provides interactive visualizations of the dataset. It helps users easily explore patterns related to thyroid cancer recurrence.

📌 Interactive dashboard includes:

• Demographics & Patient History
• Clinical Examination & Diagnosis
• Cancer Staging & Treatment Response
• Recurrence heatmaps and slicers by age, gender, pathology, etc.

🏥 Use Cases

This predictive model can be effectively integrated into Clinical Decision Support Systems (CDSS) to enhance healthcare delivery in the following ways:

• 🎯 Identify high-risk patients for follow-up
• 🧩 Personalized treatment strategies
• 🏥 Hospital resource optimization

🔮 Single Patient Prediction Module

This module allows clinicians or users to input a single patient's clinical profile and receive a prediction on whether thyroid cancer is likely to recur.

📥 Example Input Format:

⚙️ How It Works

• Input is received in dictionary format.

• Categorical values are encoded using OneHotEncoder.

• Numerical values (Age) are scaled using MinMaxScaler.

• Features are combined into a prediction-ready format.

• Random Forest model predicts:

  • ✅ Prediction (Yes / No for recurrence)

  • 📊 Probability score (confidence level)

🧠 Prediction Function

📌 Output

This means the patient is unlikely to experience recurrence, with a 97.9% confidence level.

🛠️ Tech Stack

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly)
• Power BI for visualization
• Jupyter Notebook
• joblib for model serialization

📄 Reports

A detailed project report is included in Thyroid_Disease_Prediction_Report.pdf, covering methodology, model performance, results, and recommendations.