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Improving Healthcare Services of Community Clinics using Machine Learning Techniques

机译:使用机器学习技术改善社区诊所的医疗保健服务

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Healthcare in Bangladesh has witnessed rapid growth in the recent past. A vast amount of data is generated in health sector of Bangladesh every day. Machine learning (ML) has a wide range of applications in healthcare. This paper highlights a brief overview of the applications of ML in healthcare. In this paper, we used ML techniques to predict different outpatient amounts of community clinics. We collected a dataset of 14889 patients within a time span of 508 days, from Community Clinics in Sandwip. In our research, we predicted the day of the week when maximum female and infant patients come into the community clinic for treatment. So the clinic authority may arrange support staffs accordingly. We used multiple linear regression and support vector regression for this purpose. Experimental results show that we could predict the amount of different types of outpatient visit with minimum error.
机译:孟加拉国的医疗保健最近经历了快速增长。孟加拉国卫生部门每天都会产生大量数据。机器学习(ML)在医疗保健领域具有广泛的应用。本文重点概述了ML在医疗保健中的应用。在本文中,我们使用机器学习技术来预测社区诊所的门诊量。我们从Sandwip的社区诊所收集了508天时间内14889例患者的数据集。在我们的研究中,我们预测了一周中的一天,会有最多的女性和婴儿患者进入社区诊所进行治疗。因此,诊所当局可以相应地安排支持人员。为此,我们使用了多元线性回归和支持向量回归。实验结果表明,我们可以以最小的误差预测不同类型的门诊就诊数量。

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