Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.
Data mining has ability to extract useful knowledge that is hidden in huge data. Health care system is potential area to apply and take the advantage of data mining. The causes of child illnesses and admissions to hospitals that utilize the scarce resource in sub-Saharan region were easily preventable. Higher priority was given for prevention and control of these diseases at community level. However, for seriously ill children admissions should be facilitated in order to save the life of the child. Therefore, the purpose of this study was to apply data mining techniques on underfive children dataset in developing a model that support the discovery of the causes for under-five children admission to pediatric ward. Methodology: The six-step cross industry standard process for data mining model was applied. Decision tree and artificial neural network algorithms were tested for classification. Exploratory data analysis techniques, graphs and tabular formats for visualization and accuracy, true positive rate, false positive rate, Receiver Operating Characteristic curve and the idea of experts were used for evaluation of the model. Result: A total of 11,774 instances were used to construct the decision tree and artificial neural network. The decision tree algorithm J48 has higher accuracy (94.77%), weighted true positive rate (94.7%), weighted false positive rate (5.3%), weighted Receiver Operating Characteristic curve (0.99) and performs much faster than multilayer perceptron. According to the interesting rules in J48 presenting complaint of not taking any food, fluid or breast feeding (98.32%), was the top cause of under-five children admission to pediatric ward without any consideration of health information management system admission criteria. Conclusion: In conclusion, encouraging results were obtained in classification tasks, data mining technique was applicable on pediatric dataset in developing a model that support the discovery of the causes of under-five children admission to pediatric ward.
Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi