Liver disease prediction using Ensemble Technique
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.
OUR PROPOSED ABSTRACT:
In recent decades, liver diseases have risen to prominence as one of the world’s top causes of death and a condition that can be fatal. According to the WHO, chronic diseases are responsible for over 59 percent of global mortality and 46% of conditions, and they claim the lives of almost 35 million people worldwide. As the liver continues to operate even when partially wounded, problems with the liver are typically not identified until it is too late. Potentially, early discovery can save a person’s life. This project’s need is to outline a framework for an iterative method of finding high-risk patients’ events that is based on a clinical data repository and machine learning algorithm. In order for the prediction to be accurate, the article is also open to new difficulties and potential adjustments to other cutting-edge technology. Damage to the liver, which performs a vital function for the body, could have catastrophic repercussions. Early liver disease detection is crucial for this reason. This work attempted to predict liver disease using an ensemble technique (Gradient Boosting Classifier + AdaBoost Classifier) based on various clinical data collected from healthy blood donors and liver patients.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Gradient Boosting Classifier + AdaBoost Classifier (Ensemble Technique)
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
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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