Thyroid Disease Detection

Problem Statement:

Thyroid disease is a common cause of medical diagnosis and prediction, with an onset that is difficult to forecast in medical research. The thyroid gland is one of our body's most vital organs. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body's metabolism. The main goal is to predict the estimated risk on a patient's chance of obtaining thyroid disease or not.

Demo

prediction.mp4

Technical Aspects

Python 3.7 and more
Important Libraries: sklearn, pandas, numpy, matplotlib & seaborn
Front-end: HTML, CSS
Back-end: Flask framework
IDE: Jupyter Notebook, Pycharm & VSCode
Database: SQLite
Deployment: Locally

How to run this app

Code is written in Python 3.7 and more. If you don't have python installed on your system, click here https://www.python.org/downloads/ to install.

Create virtual environment
conda create -p venv python==3.7 -y
Activate the environment
conda activate venv
Install the packages
pip install -r requirements.txt
Run the app
python main.py

Workflow

Data Collection

Thyroid Disease Data Set from UCI Machine Learning Repository.

Link:https://archive.ics.uci.edu/ml/datasets/thyroid+disease

Data Pre-processing

Drop columns not useful for training the model. Such columns were selected while doing the EDA.
Replace the invalid values with numpy “nan” so we can use imputer on such values.
Encode the categorical values
Check for null values in the columns. If present, impute the null values using the KNN imputer.
After imputing, handle the imbalanced dataset by using RandomOverSampler

Model Creation and Evaluation

Various classification algorithms like Random Forest, XGBoost, KNN etc tested.
Random Forest, XGBoost and KNN were all performed well. XGBoost, Random Forest was chosen for the final model training and testing.
Hyper parameter tuning was performed using RandomizedSearchCV
Model performance evaluated based on accuracy, confusion matrix, classification report.

Database Connection

SQLite database used for this project.

Model Deployment

The final model is deployed locally.

Batch File Prediction User Interface

User need to upload CSV file and click Custom File Predict button for prediction to start.

Prediction CSV file will contain index numer with type of thyroid disease patient is suffering from.

Experience Letter

Project Documents

initialize git repo

git init
git add .
git commit -m "first commit"
git branch -M main
git remote add origin <github_url>
git push -u origin main

to update the modification

git add .
git commit -m "proper message"
git push

Author

Vikram Jha: https://www.linkedin.com/in/vikram888/

vikram0888 / thyroid-detection Goto Github PK

thyroid-detection's Introduction