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Descriptive and Predictive Analysis of Mail Order Pharmacy
Putra H.Y.a, Khodra M.L.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Mail Order Pharmacy is an online pharmacy service which has been widely applied in developed countries. Mail Order Pharmacy has many advantages, especially in long-term treatment patients. In this experiment, Mail Order Pharmacy data of one country in Europe will be used to descriptive and predictive analysis. Descriptive analysis by building data mart basedt on the list of available decisions. Then built data visualization. Prediction using data mining consists of classification and regression. The feature set used consists of feature selection using pearson correlation and feature deletion. Experiments were performed to get the best performance using Naive Bayes, Multi Layer Perceptron and Support Vector Machine. Classification to predict order status yes or no. The best classification F-Measure using Multi Layer Perceptron is 0.577 with 10 fold cross-validation and 0.463 using data testing. Regression to predict sale-quantity. The best regression Root Mean Square Error using Multi Layer Perceptron is 1.4736 with 10 fold cross-validation and 1.6654 using data testing. Both classification and regression used to predict revenue in the next month.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]10-fold cross-validation,Analysis descriptive,Descriptive analysis,Developed countries,Multi layer perceptron,Pearson correlation,Predictive,Root mean square errors[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Analysis descriptive,MLP,MOP,NB,Predictive,SVM[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IWBIS.2018.8471714[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]