The Liver Cirrhosis Prediction is a project done as Final_requirement for FusemachinesAI Fellowship: Machine Learning
This aims to detect the stage of cirrhosis using various biomarkers of patients.
The file contains: EDA part and Prediction Part
This Flask application aims to predict the presence of liver cirrhosis in patients using a set of biomarkers. The prediction model is built upon various clinical factors and laboratory results, including the following biomarkers:
N_Days: Number of days since the initial diagnosis or assessment. Age: The age of the patient. Ascites: Presence or absence of ascites (abnormal accumulation of fluid in the abdomen). Hepatomegaly: Presence or absence of hepatomegaly (enlargement of the liver). Spiders: Presence or absence of spider angiomas (spider-like blood vessels on the skin). Bilirubin: Bilirubin levels in the blood. Cholesterol: Cholesterol levels in the blood. Albumin: Albumin levels in the blood. Copper: Copper levels in the blood. Triglycerides: Triglyceride levels in the blood. Platelets: Platelet count. Prothrombin: Prothrombin time (a measure of blood clotting time). To make a prediction, the application takes these biomarker values as input and provides an output indicating the likelihood of liver cirrhosis. The prediction model has been trained on a dataset containing a diverse range of patient samples, and it utilizes machine learning algorithms to make accurate predictions.
Clone the repository:
git clone git@github.com:rupeshghimire7/Liver_Cirrhosis_Prediction.git
Go to liver-cirrhosis-prediction file
cd liver-cirrhosis-prediction
Install the required dependencies using pip:
pip install -r requirements.txt
Run the Flask application:
cd service
python app.py
OR:
python3 app.py
Access the application through your web browser at http://127.0.0.1:5000/predict
Enter the values for the biomarkers mentioned above into the provided input fields.
Click the "Predict" button to obtain the liver cirrhosis prediction result.
