Review of Optimization-Based Feature Selection Algorithms on Healthcare Dataset

Manonmani, M.; Sarojini, B.

doi:10.1007/978-981-15-0135-7_23

Cited by 5 publications

(1 citation statement)

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“…One central challenge in many real-life applications is class imbalance ignoring which results in over-classifying the majority group due to its increased prior probability (Johnson & Khoshgoftaar, 2019 ). There are many types of class imbalance as discussed comprehensively in this review (He & Garcia, 2009 ; Manonmani & Balakrishnan, 2020 ); concerning intrinsic and extrinsic properties, “Variable factors such as time and storage also give rise to data sets that are imbalanced”. The time property is an important factor within in-hospital data casing it to be relatively highly imbalanced.…”

Section: Experimental Analysismentioning

confidence: 99%

Predicting non-attendance in hospital outpatient appointments using deep learning approach

Dashtban

2021

Health Systems

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The hospital outpatient non-attendance imposes a substantial financial burden on hospitals and roots in multiple diverse reasons. This research aims to build an advanced predictive model for predicting non-attendance regarding the whole spectrum of probable contributing factors to non-attendance that could be collated from heterogeneous sources including electronic patients records and external non-hospital data. We proposed a new non-attendance prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs) to learn the underlying manifold of data and thereby compacting information and providing a better representation that can be utilised afterwards by other learning models as well. The proposed approach is evaluated over real hospital data and compared with several well-known and scalable machine learning models. The evaluation results reveal the proposed approach with softmax layer and logistic regression outperforms other methods in practice.

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Section: Experimental Analysismentioning

confidence: 99%