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All Journal EMITTER International Journal of Engineering Technology JESCEE- Journal of Emerging Supply Chain, Clean Energy, and Process Engineering
Sharma, Naresh
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Impact of Principal Component Analysis on the Performance of Machine Learning Models for the Prediction of Length of Stay of Patients Gupta, Jagriti; Sharma, Naresh; Aggarwal, Sandeep
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.835

Abstract

Patient inflow, limited resources, criticality of diseases and service quality factors have made it essential for the hospital administration to predict the length of stay (LOS) for inpatients as well as outpatients. An efficient and effective LOS prediction tool can improve the patient care and minimize the cost of service by increasing the efficiency of the system through optimal allocation of available resources in the hospital. For predicting patient’s LOS, machine learning (ML) models can have encouraging results. In this paper, five ML algorithms, namely linear regression, k- nearest neighbours, decision trees, random forest, and gradient boosting regression, have been used to predict the LOS for the patients admitted to the hospital with some medical history, laboratory measurements, and vital signs collected before admission. Additionally, the impact of principal component analysis (PCA) has been analyzed on the predictive performance of all ML algorithms. A five-fold cross-validation technique has been used to validate the results of proposed ML model. The results concluded that the RF and GB model performs better with score of 0.856 and 0.855 respectively among all the ML models without using PCA. However, the accuracy of all the models increased with the PCA except KNN and LR. The GB model when used with principal components has score and MSE approximate to 0.908 and 0.49 respectively compared to the model that incorporates with the original data. Additionally, PCA has an advantageous effect on the DT, RF and GB models. Therefore, LOS for new patients can be predicted effectively using the proposed tree-based RF and GB model with using PCA.
Enhancing Patient Experience in Radiology: Predictive Modeling of Wait Times using Feature Selection Techniques Gupta, Jagriti; Sharma, Naresh
Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Vol 4 No 1 (2025): Journal of Emerging Supply Chain, Clean Energy, and Process Engineering
Publisher : Universitas Pertamina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57102/jescee.v4i1.103

Abstract

The increasing patient flow and overcrowding in critical hospital departments has prompted the need for effective strategies to enhance patient satisfaction. This study focuses on machine learning algorithms to predict patient waiting times for X-ray services using the dataset from a high-volume radiology department. Three regression models such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF) were proposed and integrated with the recursive feature elimination (RFE) algorithm to reduce the dimension of the dataset and to enhance the model’s efficiency by selecting optimal features. The findings indicate that LR-RFE model with 30 features predicted waiting time with mean absolute error 3.63 minutes as compared to standard LR model with 63 features. Comparable results were observed with the RF and KNN models, which demonstrated mean absolute errors of 3.77 minutes and 3.81 minutes respectively. Furthermore, the feature revealed key contributors to waiting times, such as the sum of patient queue wait times, the number of patients waiting in line, and wait time for the most recent patient This study underscores the potential of machine learning techniques combined with feature selection to offer actionable insights for better patient queue management.
Co-Authors Aggarwal, Sandeep Gupta, Jagriti