Hospital Appointment Prediction Using Classification of Big Data

Authors

  • G. Adi Narayana
  • K. Stewart Kirubakaran
  • K. Logu

Abstract

A continual drawback in tending is that the high share of patients World Health Organization miss their appointment, be it a consultation or a hospital check. This study seeks patient’s activity patterns that permit predicting the chance of no-shows. We have a tendency to explore the convenience of victimisation Machine Learning models to accomplish this task. This work involves the preliminary information analysis of the 100k medical appointments in brazil and it's centered on the question of whether or not or not patients show up for his or her appointments. to research the info validation, information cleaning/preparing and information visual image are going to be done on the whole given dataset. for every combine of variables, calculate the proportions of class combos to spot the biggest cluster of patients World Health Organization didn’t show-up. The target of this analysis is to function a start line to distinguishing the factors that they will be contributive to the patients missing their appointments. to boot, to check and discuss the performance of comparative study with finding the simplest accuracy apply in numerous supervised machine learning technique from the given dataset with interface based mostly application by given dataset attributes.

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Published

2019-12-26

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Section

Articles