DEVELOPING CLASSIFICATION MODEL WITH KNOWLEDG BASE SYSTEM FOR DIAGNOSIS AND TREATMENT RECOMMENDATION OF HOSPITAL ACQUIRED PNEMONIA

Abstract

Pneumonia is an illness, usually caused by infection, in which the lungs become inflamed and congested, reducing oxygen exchange and leading to cough and breathlessness. It affects individuals of all ages but occurs most frequently in children and elderly. Pneumonia has different categories. Hospital Acquired Pneumonia (HAP) is the first in mortality and morbidity. And lack of health facilities, lack of sufficient professional in hospitals and complexity of the diagnosis process are problems exposed. KBS has great role in the health care sector so this study aims to combine data mining results with expert knowledge, and establish a Knowledge Base System for the diagnosis and treatment recommendation of HAP. Design science research methodology, with a hybrid data mining process model was employed. The researcher gathered a dataset of 3244 cases of Hospital Acquired Pneumonia (HAP) from Werabe Referral Hospital. The random forest, J48, JRip, and PART algorithms were used in 4 tests with two distinct scenarios by the researcher in order to create the classifier model. PART classifier algorithm conducted on selected attributes with percentage split test option with an accuracy of 99.3% was achieved. To model the gained knowledge decision tree modeling technique and to represent the gained knowledge the rule-based knowledge representation technique was used. Semi structured interview technique is chosen for acquiring knowledge from expert. Then it is modeled by using the decision tree modeling techniques and represented in the production rule. The two extracted knowledge was combined and checked for rule redundancy to develop the knowledge-based system. Finally, to develop the KBS the researcher used SWI prolog and Net Beans for making user interface. To evaluate performance of the developed system, the researcher has used system performance testing and user acceptance evaluation. And Achieves 90.3% accuracy for system performance. And 91.3% of accuracy for user acceptance testing. The result show that the developed system achieves good performance and meets the objectives of the study and it could give proper treatment. This deduces that the developed system could help in identifying the severity level and in diagnosis and treatment recommendation of Hospital Acquired Pneumonia.