MAPPING AND PREDICTION OF COMMUNITY HEALTH LEVEL IN THE EAST JAVA REGION USING ANN BACKPROPAGATION METHOD

Muhammad Nurrohim, Wiwiet Herulambang, Fardanto Setyatama
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引用次数: 1

Abstract

Predicting the level of public health in an area is very important to know the development of public health in order to give consideration to local governments in making policies that can increase the level of health and health services for the community. So we need a system that is able to make predictions and describe the development of public health in the years to come. The data used in the study were the percentage of the population who had health complaints and outpatient treatment for a month, the percentage of households that had access to proper sanitation, the percentage of households that had access to decent drinking water sources, the percentage of poor people, the average length of schooling, infant mortality rate, number of health centers and life expectancy. Backpropagation neural network method is a method that is often used in forecasting or prediction. Where this method can identify activities based on past data. Past data will be studied by neural networks so that they have the ability to make decisions on data that has never been studied. This method is expected to provide a prediction of the level of health by studying data from previous years. The results of this study produced a predictive map of the level of public health in the East Java region in 2019, 2020 and 2021 based on the model obtained from the training results with an epoch of 100 and an MSE value of 0.0173254.
基于Ann反向传播方法的东爪哇地区社区卫生水平映射与预测
预测一个地区的公共卫生水平对于了解公共卫生的发展非常重要,从而为地方政府制定能够提高社区卫生水平和卫生服务水平的政策提供参考。因此,我们需要一个能够预测和描述未来几年公共卫生发展的系统。研究中使用的数据是:一个月内因健康问题就诊并接受门诊治疗的人口百分比、享有适当卫生设施的家庭百分比、享有体面饮用水源的家庭百分比、穷人百分比、平均受教育年限、婴儿死亡率、保健中心数目和预期寿命。反向传播神经网络方法是一种经常用于预测或预测的方法。这种方法可以根据过去的数据识别活动。过去的数据将被神经网络研究,这样他们就有能力对从未被研究过的数据做出决定。这种方法有望通过研究前几年的数据来预测健康水平。该研究结果基于从训练结果中获得的模型,生成了2019年、2020年和2021年东爪哇地区公共卫生水平的预测图,历元为100,MSE值为0.0173254。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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