GitHub - erfury/QUALITATIVE-ANALYSIS-OF-MACHINE-LEARNING-ALGORITHMS-USING-LIVER-DISEASE-DATASET: The Medical diseases analysis is emerging in the area of research. In recent years, various attempts are made for the creation of computer aided diagnosis applications. Due to errors in medical diagnostics systems can result in seriously misleading the treatment of patients. Machine learning finds various applications in the areas including computer aided diagnosis. After converting subject in equation disease can be indicated accurately. For the analysis of multi model bio medical data, machine learning offer the convenient approach for making classy and automatic algorithms. This project provides the comparative analysis of different machine learning algorithms for detection of liver disease. The liver diseases are one of the most prevalent chronic diseases, worldwide. It is proved to be based on multi factors caused by complex interactions involving the genetic, epigenetic and environmental factors. This project demonstrate and analytical approach for prediction of liver diseases in patients using probabilistic model based on Artificial Neural network (ANN), KSVM, SVM, Naïve Baye’s. The technique used for classification and prediction are based on recognizing typical and diagnostically most important clinical features considered responsible for Liver diseases. These clinical features are provided as input to the classification model for prediction and qualitative analysis. The main contribution of project involve developing of classifier model based on the above mentioned machine learning algorithms. The analysis confirmed high risk and low risk patients based on the predictions by the probabilistic model. The qualitative parameters involved in the research are Accuracy, Specificity and Sensitivity.

erfury / QUALITATIVE-ANALYSIS-OF-MACHINE-LEARNING-ALGORITHMS-USING-LIVER-DISEASE-DATASET Public

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The Medical diseases analysis is emerging in the area of research. In recent years, various attempts are made for the creation of computer aided diagnosis applications. Due to errors in medical diagnostics systems can result in seriously misleading the treatment of patients. Machine learning finds various applications in the areas including comp…