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FIGURE 1. Patient gender count(Left), Grouping by age wise(Right). TABLE 1. An outline of the patient dataset for liver disease.

Figure 1 Patient gender count(Left), Grouping by age wise(Right). TABLE 1. An outline of the patient dataset for liver disease.

Related Figures (10)

FIGURE 2. Classification of diseased person based on age(Left), Healthy and Unhealthy liver ratio(Right).
FIGURE 2. Classification of diseased person based on age(Left), Healthy and Unhealthy liver ratio(Right).
FIGURE 5. Multilayer Perceptron implementation by Layer wise. Since our datasets have been standardized and scaled down to be between | and -1, we chose a 3-layer neural network with the Tanh and Sigmoidal activation functions to handle the highly overlapping and non-linear nature of the data which can produce better accuracy. Then its procedures are given as Figure 5. Since our datasets have been standardized and scaled down
FIGURE 5. Multilayer Perceptron implementation by Layer wise. Since our datasets have been standardized and scaled down to be between | and -1, we chose a 3-layer neural network with the Tanh and Sigmoidal activation functions to handle the highly overlapping and non-linear nature of the data which can produce better accuracy. Then its procedures are given as Figure 5. Since our datasets have been standardized and scaled down
FIGURE 6. Voting classifier procedure.
FIGURE 6. Voting classifier procedure.
FIGURE 7. Pair-plots among MLP, SVM, KNN, and HVC.
FIGURE 7. Pair-plots among MLP, SVM, KNN, and HVC.
FIGURE 9. Compared models using evaluating criteria. FIGURE 8. Correlation matrix based on the Attribute of the Liver diseased data set. so we have to choose a non-linear model function for this dataset.
FIGURE 9. Compared models using evaluating criteria. FIGURE 8. Correlation matrix based on the Attribute of the Liver diseased data set. so we have to choose a non-linear model function for this dataset.
FIGURE 10. Individual models vs criteria comparison.
FIGURE 10. Individual models vs criteria comparison.
TABLE 2. Result of models using various evaluating criteria.
TABLE 2. Result of models using various evaluating criteria.
FIGURE 11. Model confusion Matrices among MLP, SVM, KNN and HVC.
FIGURE 11. Model confusion Matrices among MLP, SVM, KNN and HVC.
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