Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

This project leverages machine learning to predict liver disease using clinical data from the Indian Liver Patient Dataset. It combines exploratory data analysis (EDA), classification, and regression modeling to extract meaningful healthcare insights.

Machine learning is one of the most critical aspects of automated disease diagnosis and disease prediction. It involves data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one of the most fatal diseases in several countries. In this thesis, liver patient datasets are investigate for building classification models in order to predict liver disease. This thesis implemented a feature model construction and comparative analysis for improving prediction accuracy of Indian liver patients in three phases. In first phase, min max normalization algorithm is applied on the original liver patient datasets collected from UCI repository. In liver dataset prediction second phase, by the use of PSO feature selection, subset (data) of liver patient dataset from whole normalized liver patient datasets is obtained which comprises only significant attributes. Third phase, classification algorithms are applied on the data set. In the fourth phase, the accuracy will be calculated using root mean Square value, root mean error value.J48 algorithm is considered as the better performance algorithm after applying PSO feature selection. Finally, the evaluation is done based on accuracy values.

HealthOrzo is a Disease Prediction and Information Website. It is user friendly and very dynamic in it's prediction. The Project Predicts 4 diseases that are Diabetes , Kidney Disease , Heart Ailment and Liver Disease . All these 4 Machine Learning Models are integrated in a website using Flask at the backend .

A simple Liver Disease Prediction App built using Streamlit and Machine Learning. This app predicts the presence of liver disease based on health parameters. 🏥🩺

🩺 Quadra-Diag: Multi-Disease Prediction System A machine learning-based prediction system that identifies Heart Disease, Liver Disease, Parkinson’s, and Diabetes using Flask API. The model utilizes XGBoost, SVM, and Logistic Regression for accurate diagnosis based on patient data. 🔹 Key Features: ✅ Predicts multiple diseases in one model ✅ Uses

This is a complete all in one website which is used in the prediction of various diseases like heart disease,kidney disease,liver disease,diabetes disease and cancer detection using many Machine Learning algorithms like RandomForestClassifier, XGBoost, Knn etc.It performs with a high accuracy to know the exact status of the diseases.

This study examines the prediction of liver disease using machine learning algorithms like K-Nearest Neighbors, Gradient Boosting, Decision Tree, and Random Forest utilizing a dataset of 1700 records with patient demographics, lifestyle factors, family history, and medical profiles,

This repository contains an analysis of the Indian Liver Patient Dataset, using various machine learning models to predict the presence of liver disease.

Prediction project for patients with liver disease using Machine Learning algorithms.

Data analysis and classification project for Liver Disease Prediction using machine learning algorithms

Early liver disease prediction using machine learning (for affordable diagnosis in developing countries)

A Prediction Model of Detecting the Liver Diseases in Patients using Logistic Regression of Machine Learning

A machine learning–based liver disease prediction system using KNN, SMOTE, and clinical data, featuring automated preprocessing, model evaluation, and an interactive prediction interface.

Liver Disease Prediction App – A web-based application that predicts liver disease risk using machine learning models. Users enter health parameters, and the system provides early predictions with risk levels. Built with Flask/Django, scikit-learn, and a simple, user-friendly interface for healthcare use.

🧬 Advanced AI-powered Disease Prediction System 🤖 using Machine Learning & Explainable AI (SHAP/LIME). Predicts risks for Diabetes, Heart, Liver & Kidney diseases 💉. Includes preprocessing, model tuning, web app (Streamlit/Flask) & real-time prediction dashboard 📊

A machine learning-based web application for predicting multiple diseases — including diabetes, heart disease, Parkinson’s, kidney disease, and liver disease — using user input and trained classification models. Built with Python, Streamlit, and scikit-learn for fast and accurate predictions.

🏥 AI-powered disease prediction system using machine learning to analyze symptoms and predict potential health conditions. Supports diabetes, heart disease, liver disease, and Parkinson's detection with 90%+ accuracy. Built with Python, Streamlit, and scikit-learn for accessible healthcare insights.

A web-based application that predicts multiple diseases including diabetes, heart disease, stroke, chronic kidney disease, and liver disease using machine learning models. The system leverages optimized ML algorithms to provide accurate predictions and offers a user-friendly interface for input and result visualization.

Disease Prediction System is a Streamlit-based web app that predicts Diabetes, Heart Disease, and Fatty Liver using machine learning models, and provides personalized health guidance through a Gemini-powered AI assistant with a modern, interactive UI.

Intelligent Clinical Decision Support System (CDSS) for automated prescription analysis and multi-disease risk prediction. Supports handwritten and digital prescriptions using OCR and Clinical NER with machine learning models for diabetes, obesity, liver and cardiovascular risk prediction.

Machine learning project predicting liver disease risk. Features pre-processed for quality, with insights from EDA. Models optimized using GridSearchCV, and performance compared via paired t-tests. Delivers a robust prediction model with key insights for accuracy improvement.

Liver Disease prediction using binary classification such as SVM, ANN, or Random Forest. Generate missing data using the MICE algorithm. Use SMOTE to oversample minority class to reduce biases towards majority class. ROC analysis and k-fold Cross-validation Hypothesis tests were done. Data Source: UCI Machine Learning Repository

The Medical diseases analysis is emerging in the area of research. In recent years, various attempts are made for the creation of computer aided diagnosis applications. Due to errors in medical diagnostics systems can result in seriously misleading the treatment of patients. Machine learning finds various applications in the areas including computer aided diagnosis. After converting subject in equation disease can be indicated accurately. For the analysis of multi model bio medical data, machine learning offer the convenient approach for making classy and automatic algorithms. This project provides the comparative analysis of different machine learning algorithms for detection of liver disease. The liver diseases are one of the most prevalent chronic diseases, worldwide. It is proved to be based on multi factors caused by complex interactions involving the genetic, epigenetic and environmental factors. This project demonstrate and analytical approach for prediction of liver diseases in patients using probabilistic model based on Artificial Neural network (ANN), KSVM, SVM, Naïve Baye’s. The technique used for classification and prediction are based on recognizing typical and diagnostically most important clinical features considered responsible for Liver diseases. These clinical features are provided as input to the classification model for prediction and qualitative analysis. The main contribution of project involve developing of classifier model based on the above mentioned machine learning algorithms. The analysis confirmed high risk and low risk patients based on the predictions by the probabilistic model. The qualitative parameters involved in the research are Accuracy, Specificity and Sensitivity.

No description available

Frontend:HTML,CSS,JavaScript Backend:Python with Django

Liver Disease Prediction using Machine Learning

Machine learning-based classification system for predicting liver disease using clinical and biochemical patient data (ILPD dataset).

No description available

This is repo that contains various type of machine learning algorithm to predict Liver Disease...