click here to download project abstract related to morality prediction using machine learning.
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ABSTRACT
Children Mortality refers to mortality of children under the age of five. The child
mortality rate, also under-five mortality rate, refers to the probability of dying
between birth and exactly five years of age. The mortality of children also happens
in fetus. The aim is to investigate machine learning based techniques for
classification of mortality fetal health classification results in best accuracy. The
analysis of dataset by supervised machine learning technique (SMLT) to capture
several information’s like, variable identification, uni-variate analysis, bi-variate
and multi-variate analysis, missing value treatments and analyze the data
validation, data cleaning/preparing and data visualization will be done on the entire
given dataset. Our analysis provides a comprehensive guide to sensitivity analysis
of model parameters with regard to performance in the classification of fetal
health. To propose a machine learning-based and additionally, to compare and
discuss the performance of various machine learning algorithms for the given
dataset. This article helps you to understand how can we use machine learning for child morality prediction.
