DESIGNING ADULT MORTALITY PREDICTION MODEL FROM  ADMITTED PATIENT RECORDS USING DATA MINING  TECHNIQUES

SHUMET, MOLLA WASSIE

dc.contributor.author	SHUMET, MOLLA WASSIE
dc.date.accessioned	2022-03-18T06:40:01Z
dc.date.available	2022-03-18T06:40:01Z
dc.date.issued	2021-09-13
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/13212
dc.description.abstract	Clinical data refers to health-related information that is associated with regular patient care. It provides information to health care professionals to improve the quality and safety of the care they provide to their patients. Based on huge data mining research is used to improve health care, to plan and make decision policy for satisfy medication process. Hence, health service planning and utilization become limited. Thus, adult mortality levels and trends in the developing countries become hampered. Therefore, in this study, we proposed a data mining prediction model to identify determinant attribute and consideration factors for adult mortality in case of patient dataset in Felege Hiwot Referral Hospital. The study contains 7095 instances from Felege Hiwot referral hospital recorded datasets that age between 15-60 years. To develop the model, we used classification techniques and data mining algorithm such as J48 decision tree algorithm, Support vector Machine, Random Tree, K-Nearest Neighbor and Naïve Bayes algorithm. In this research Attribute selection for better accuracy is performed by using GainRatioAtrributeEval with rank. We used data KDD model approach to processed data. K-Nearest Neighbor algorithm is selected algorithm to build the model that predict adult mortality in better accuracy and possible for correct classification with value 88.27% and K-Nearest Neighbor was processed in 0 second speed, 88.3% recall, 88.9% precision and 88.5% F-Measure scored in this research. Finally, National Classification of Disease, Duration of illness, Length of stay was significantly selected attribute to predict adult mortality. From this research Urinary Tract Infection, tuberculosis, congestive heart failure; renal disease, Road traffic accident and Poisoning were main factors of adult mortality which needed attention to minimize adult mortality by found the cause of establishment of such diseases and provided community awareness. From this study K-nearest neighbor is recommended Algorithm for build model to predict adult mortality with 88.27% accuracy was possible. Duration of illness was very dominant attribute to adult mortality in Felege Hiwot Referral Hospital in this study. Keyword: National classification of disease, mortality, length of stay, KNN, clinical data	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	DESIGNING ADULT MORTALITY PREDICTION MODEL FROM ADMITTED PATIENT RECORDS USING DATA MINING TECHNIQUES	en_US
dc.type	Thesis	en_US