Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Prediction

2017 ◽  
Author(s):  
Mafazalyaqeen Hassoon ◽  
Mikhak Samadi Kouhi ◽  
Mariam Zomorodi-Moghadam ◽  
Moloud Abdar
Keyword(s):  
Genetic Algorithm ◽  
Liver Disease ◽  
Disease Prediction ◽  
Rule Optimization

scholarly journals New Improved Genetic Algorithm for Coronary Heart Disease Prediction

2016 ◽  
Vol 136 (5) ◽  
pp. 34-37 ◽  
Author(s):  
Waheeda Dhokley ◽  
Tahreem Ansari ◽  
Naeema Fazlani ◽  
Heena Mohd.Hafeez
Keyword(s):  
Genetic Algorithm ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Disease Prediction ◽  
Improved Genetic Algorithm

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

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