Liver disease prediction using Ensemble Technique | Python Final Year IEEE Project.
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📌IEEE Base Paper Title: Liver disease prediction using Ensemble Technique.
💡Implementation Code: Python.
🚀Algorithm / Model Used: Gradient Boosting Classifier + AdaBoost Classifier (Ensemble Technique).
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/.
IEEE Base paper Abstract:
Liver illness is one of the worst diseases on the planet. It occurs in the human body, most notably in the liver. The liver’s primary function is to eliminate waste created by organisms, to store key vitamins required by the body so that they do not go to waste, and to digest meals. This is a highly terrible disease, and the first thing that has to be done is to limit the risk explored by this lethal disease, and early detection can assist save the organism. The amount of people that are disease in the world is approx. 3.5 percent. The number of advancements that are happening in prediction of the disease done through the help of machine learning classification techniques like KNN, random forest SVM, and logistic regression. Other deep learning methods are also incorporated to solve this problem such as artificial neural network and convolution neural network. The methods would definitely increase the life expectancy of the patient suffering from this disease and avoid the chronic liver disease (CLD). The data may be gathered in enormous quantities as a result of the widespread use of bar codes for superior marketable items, the automation of many commercial and government transactions, and the advancement of data gathering systems. The proposed system that has been used ensemble methods such as random forest, xgboost and gradient boost and are combined to get a greater accuracy.
REFERENCE:
Sai Rohith Tanuku, Addike Ajay Kumar, Sai Roop Somaraju, Rushitaa Dattuluri, Madana Vamshi Krishna Reddy, Sambhav Jain, “Liver disease prediction using Ensemble Technique”, 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE Conference, 2022.
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