Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i11/67151
Year: 2016, Volume: 9, Issue: 11, Pages: 1-9
Original Article
V. R. Suma, Shwetha Renjith, Sreeja Ashok* and M. V. Judy
Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidya peetham, Kochi - 682024, Kerala, India; [email protected], shweshweranj[email protected], [email protected], [email protected]
*Author for Correspondence
Sreeja Ashok Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidya peetham, Kochi - 682024, Kerala, India; [email protected]
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 root 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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