Indian Journal of Science and Technology

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The objective of this paper is to analyze and identify the best classification solution for clinical decision making. Several classification algorithms Like Discriminant Analysis (LDA), Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), and Decision Trees are compared to find the optimum diagnostic accuracy. The performance of classification algorithms are compared using benchmark dataset, breast cancer. The effects of normalization using z-score and min-max approaches are also investigated. The results are compared based on different performance parameters like accuracy, sensitivity, specificity and ischolar_main node error value. Accuracy has been improved for all classifications methods after normalizing the data set. Z-score normalization performs better for all the measures when compared to min-max normalization. The proposed approach shows higher accuracy rate for Naive Bayes algorithm when compared with the other algorithms.

Keywords

Accuracy, Classification, Min-Max Normalization, Sensitivity, Specificity, Z-Score Normalization
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