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Prediction of Hepatitis Disease Using Machine Learning Technique
- Source :
- 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The objective of this work is to choose the best tool for diagnosis and detection of Hepatitis as well as for the prediction of life expectancy of Hepatitis patients. In this work, a comparative study between various machine learning tools and neural networks were carried out. The performance metric is based on the accuracy rate and the mean square error. The Machine Learning (ML) algorithms such as Support Vector Machines (SVM), K Nearest Neighbor (KNN) and Artificial Neural Network (ANN) were considered as the classification and prediction tools for diagnosing Hepatitis disease. A brief study on the above algorithms were performed based on the prediction accuracy of disease diagnosis. All the ML algorithms were implemented and validated using MATLAB software.
- Subjects :
- Artificial neural network
Mean squared error
business.industry
Computer science
Physics::Medical Physics
Confusion matrix
Machine learning
computer.software_genre
k-nearest neighbors algorithm
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Software
Artificial intelligence
business
MATLAB
Performance metric
computer
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
- Accession number :
- edsair.doi...........dc95e63dc136102c9fedfe2e3bfa1921
- Full Text :
- https://doi.org/10.1109/i-smac47947.2019.9032585