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Efficient online medical store finding and availability of medicines using Decision Tree compared with Random Forest for improved accuracy
Conference paper   Open access   Peer reviewed

Efficient online medical store finding and availability of medicines using Decision Tree compared with Random Forest for improved accuracy

Rasim Mahesh Naidu, V. Nagaraju, Afolake O. Adedayo and Celestine Iwendi
ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (University of Bolton, 24/02/2023–25/02/2023)
17/03/2023

Abstract

Materials and Methods: Both the Decision Tree (N=10) and Random Forest algorithms (N=10) were iterated 20 times with different test sizes for the Online Medical Store Finding And Availability Of Medicines and their accuracies where noted. The dataset used for this experimental research consists of 501 records. Results: Decision Tree is substantially more accurate (91.47%) than Random Forest (86.45%). The statistical significance of the Online Medical Store Finding And Availability is (p<.005 Independent sample T-test) and This score indicates that the study's results are statistically significant.. Conclusion: Compared to Random Forest, the accuracy performance parameter of the Decision Tree looks to be greater.
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