Open AccessPREDICTIVE MODEL FOR CHILD DELIVERY
Author(s) -
A. K. Abdulmumini,
G. Obunadike,
E. Jiya
Publication year - 2022
Publication title -
fudma journal of sciences
Language(s) - English
Resource type - Journals
ISSN - 2616-1370
DOI - 10.33003/fjs-2022-0601-885
Subject(s) - python (programming language) , caesarian section , recall , vaginal delivery , computer science , software , medicine , pregnancy , psychology , programming language , genetics , cognitive psychology , biology
Antenatal care is an essential period in which medical experts examines pregnant women to prepare them for proper child delivery. Choosing or knowing the likely mode of child delivery is essential both for the mother and the medical team. It helps in proper preparation for labour and any possible complication that could arise. There are also chances of reducing maternal or child mortality. However, the decision on which of the options is appropriate is sometimes difficult due to several parameters and variable. Analyzing Obstetric and mode of delivery for pregnant woman is tedious, therefore, this work used data from three medical facilities in Katsina State and apply three machine leaning algorithms to predict the most appropriate mode of child delivery. The work was implemented using python programming language software. The result of the work has shown that random forest algorithm performs better with accuracy result as precision was 0.918, recall of 0.715 and 0.896 for Spontaneous Vagina delivery and 0.716 for precision, 0.929 for recall and 0.896 for Caesarian section mode