BINARY LOGISTICS REGRESSION MODEL TO IDENTIFY FACTORS ASSOCIATED WITH LOW BIRTH WEIGHT (LBW) (CASE STUDY: BABY DATA AT DR. M. HAULUSSY HOSPITAL AMBON)
Abstract
Low birth weight (LBW) is one of the risk factors for increasing baby mortality. LBW is characterized by a baby's birth weight of fewer than 2500 grams which is weighed within the first hour after birth. The case of LBW is of special concern because it can have a serious impact on the quality of future generations, which will slow down the growth and development of children and affect the decline in intelligence. In this study, identification was carried out to determine the factors that influence the status of BBL in RSUD Dr. M. Haulussy Ambon in 2020, the data used in this study is medical record data from RSUD Dr. M. Haulussy Ambon in 2020 with a total sample of 183 respondents with predictor variables covering nine variables and one response variable. The analysis used is a binary logistic regression method with the response variables of BBL status which are categorized as normal and low. The results of this study obtained a binary logistic regression model in which the factors that influence the case of low birth weight are maternal gestational age and parity with a classification accuracy of 91.8.
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