Welcome to the Liver Cirrhosis Prediction System! Our goal is to empower healthcare professionals with advanced tools for early detection of liver cirrhosis, enabling timely medical intervention and personalized patient care.
In India, delayed diagnosis of diseases is a fundamental problem due to a shortage of medical professionals. A typical scenario, prevalent mostly in rural and somewhat in urban areas is:
- A patient going to a doctor with certain symptoms.
- The doctor recommending certain tests like blood test, urine test etc depending on the symptoms.
- The patient taking the aforementioned tests in an analysis lab.
- The patient taking the reports back to the reports back to the hospital, where they are examined and the disease is identified.
This project aims to reduce the time delay caused due to the unnecessary back and forth shuttling between the hospital and the pathology lab. Here a machine learning algorithm will be trained to predict a liver disease in patients using a data-set Indian Liver Patient Dataset (ILPD) provides a wealth of clinical data containing a variety of attributes suitable for comprehensive analysis and predictive modeling in liver disease research collected from Kaggle.
Liver cirrhosis is a serious and progressive liver disease that can lead to severe complications and death if not detected and treated early. Currently, the detection and diagnosis of liver cirrhosis rely heavily on invasive procedures and clinical assessments, which may not always be timely or accessible to all patients. The Liver Cirrhosis Prediction System aims to provide healthcare professionals with a non-invasive and efficient method for assessing a patient's risk of developing liver cirrhosis, utilizing machine learning models and patient data.
Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.
- @ShreyasMali - Shreyas Mali
- @PrasannaPandhare - Prasanna Pandhare
- @VaishnaviNaik - Vaishnavi Naik
- @VirendraMuley - Virendra Muley
The Liver Cirrhosis Prediction System utilizes a comprehensive dataset collected from Kaggle that includes various clinical and demographic data points related to liver cirrhosis. The dataset is carefully validated and cleaned to ensure high-quality data for training and testing machine learning models. Given a dataset containing various attributes of 584 Indian patients, use the features available in the dataset and define a supervised classification algorithm which can identify whether a person is suffering from liver disease or not. This data set contains 416 liver patient records and 167 non- liver patient records. This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90"
The dataset for the Liver Cirrhosis Prediction System consists of the following columns:
- Age: Patient's age.
- Gender: Patient's gender.
- Total Bilirubin: The total amount of bilirubin in the blood, a yellow pigment produced by the breakdown of red blood cells.
- Direct Bilirubin: The direct fraction of bilirubin, specifically associated with liver function.
- Total Proteins: The total amount of proteins in the blood, including albumin and globulins.
- Albumin: A protein synthesized by the liver, crucial for maintaining blood volume and pressure.
- A/G Ratio: The ratio of albumin to globulins, providing insights into liver and kidney function.
- SGPT (Serum Glutamic Pyruvic Transaminase): An enzyme indicating liver health; elevated levels may suggest liver damage.
- SGOT (Serum Glutamic Oxaloacetic Transaminase): Another liver enzyme reflecting liver function; elevated levels may indicate liver problems.
- Alkphos (Alkaline Phosphatase): An enzyme associated with the biliary system; elevated levels may indicate liver or bone issues.
👉 Home Page of LCP
👉 Admin's Workspace
👉 Admin adding Doctor
👉 Doctor's Home Page
👉 Report View
👉 Admin adding Lab Assistant
👉 Lab Assiatant registering Patient
👉 Report View
- High Accuracy: Leverage Random Forest models for precise predictions of liver cirrhosis risk.
- User-Friendly Interface: A seamless, intuitive web interface designed for medical practitioners.
- Secure and Compliant: Adhering to data privacy and healthcare regulations, ensuring safe handling of patient data.
- Predictive Modeling: Leverage machine learning algorithms for accurate liver cirrhosis risk assessment.
- Data Visualization: Clear and concise results presented for easy interpretation and action.
- Interoperability: Seamless integration with existing healthcare systems and databases.
- Log In: Authenticate as a user (doctor, operator, etc.).
- Input Data: Provide patient information and clinical data.
- View Predictions: Access liver cirrhosis risk assessments and reports.
- Improved Patient Outcomes: Early detection leads to better treatment and prognosis.
- Streamlined Workflow: Enhance healthcare efficiency and resource allocation.
- Contribute to Research: Advance medical knowledge through data-driven insights.