CLASSIFICATION AND ANALYSIS OF BIGDATA USING HOSPITAL APPOINTMENT PREDECTION SYSTEM
DOI:
https://doi.org/10.61841/4hcecd12Keywords:
appointment, examination, comparing and discussing, combinations, attributesAbstract
The high percentage of patients missing their appointment, be it a consultation or a medical examination, is a recurrent issue in healthcare. The present study seeks behavioral trends for patients that allow predicting the probability of no-shows. We are investigating the ease of using Machine Learning models to perform this function This research includes the exploratory data analysis of the 100,000 medical appointments in Brazil and focuses on whether or not patients are turning up for appointments. Data cleaning, preparation and analysis will be performed on the whole data collection to evaluate the validity of the data. For every two variables, Calculate the percentages of combinations of groups to classify the largest number of patients who did not turn up. The purpose of this study is to serve as a starting point for identifying the factors that can contribute to the patients who miss their appointments. In addition, comparing and discussing the performance of comparative analysis with finding the best accuracy is applied by given dataset attributes in different supervised machine learning techniques from the data set with interface-based application.
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