Analyzing and implementing feature selection method for predicting patient waiting time in clinics

Onn, Siti Nooraziqa (2024) Analyzing and implementing feature selection method for predicting patient waiting time in clinics. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Queuing is an unavoidable aspect of life, and not knowing how long the wait will be leads to a major source of anxiety. Few companies frequently try to forecast the waiting times problem but none of them are successful. On the other hand, length of waiting time in queue is basically major problem patients need to withstand in hospitals and clinics. It leads to unaffected implementation of medical attention and quality care to those really in needs. In queuing system, advanced approaches for instance machine learning and deep learning have played an important role. This project aims to identify the important features in predicting patients waiting time in clinics. In choosing the important features, preprocessing data is accomplished first which involve preparing raw data and constructing the data to be competent to machine learning. After that, the data will be retrieved for feature selection with different feature selections method. In this project, implementation of correlation-based feature selection, Recursive Feature elimination and Sequential Feature Selection are developed using the same dataset. Thus, to analyse the accuracy of selected features, predictive analysis is implemented, and the performance of the predicted model is analysed using primary features and selected features

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