Diagnosis of Liver Disease using ANN and MLAlgorithms with Hyperparameter Tuning

Aim:

         The aim of this project is to develop a system for the diagnosis of liver disease using Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms with hyperparameter tuning. The project focuses on leveraging advanced models and optimization techniques to enhance predictive capabilities, aiding in the early detection and effective management of liver disease.

Abstract:

          Chronic liver disease is a leading cause of morbidity and mortality worldwide, making early detection and accurate diagnosis essential for improving patient outcomes. This project proposes a machine learning-based approach to enhance the prediction and early detection of chronic liver disease. By utilizing machine learning algorithms such as Decision Tree, Random Forest, Gradient Boosting and SVM the model aims to accurately predict the presence of liver disease using relevant patient data.

      The proposed system focuses on optimizing feature selection and dataset preprocessing to improve prediction accuracy. It provides a user-friendly web application, allowing users to input data, receive predictions, and access early diagnostic insights. This solution aims to improve clinical decision-making and contribute to the timely management of chronic liver disease, reducing healthcare burdens and enhancing patient care.

Proposed System:

             The proposed system aims to overcome the limitations of existing approaches by developing a machine learning-based framework focused on the early detection of chronic liver disease. It utilizes algorithms like Decision Tree, Random Forest, Extra Tree Classifier, artificial neural network (ANN), Gradient Boosting, and Support Vector Machine (SVM) to predict the likelihood of liver disease based on relevant patient data. Key aspects of the system include optimized feature selection, dataset preprocessing, and hyperparameter tuning using GridSearchCV to improve prediction accuracy and reliability.

           The proposed system will include a user-friendly web application built with Flask, Python, HTML, CSS, JavaScript, and Bootstrap, allowing users to input data, receive predictions, and gain early diagnostic insights. By integrating GridSearchCV, the system ensures that the machine learning models are fine-tuned to their optimal configurations, further enhancing their predictive performance. By offering an accessible interface and reliable predictions, the system aims to assist healthcare professionals in making informed decisions, ensuring early intervention, and ultimately improving patient care.

Advantages:

• The system enables early detection of chronic liver disease, allowing for timely intervention and improved patient outcomes.
• The web application is designed to be intuitive and accessible, allowing healthcare professionals to easily input patient data and receive predictions.
• By automating the detection process, the system reduces the need for expensive traditional diagnostic procedures, helping to lower healthcare costs.
• The web-based nature of the system makes it scalable and easily accessible, allowing for widespread use in various healthcare settings