An Investigation on Managing Patient Flow at Hospital Emergency Care Unit Using Tree-Based Data Mining Techniques

Sultana, Rubeena; Rawat, Sandeep Singh; Murthy, G. Vishnu; Kumar, Naveen

doi:10.1007/978-981-15-3338-9_28

Cited by 1 publication

(2 citation statements)

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“…Many findings comparing the accuracy of different machine learning algorithms against the similarity search have been conducted (1)(2)(3)(4)(5)(6)(7) . Zainab Imtiaz et al (1) conversed about a model that determines pairs of duplicate queries by merging word embedding techniques such as FastText Crawl, FastText Crawl Subword and Google News Vector with separation techniques.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Similar Question Pairs Using Machine Learning Techniques

Anishaa¹,

Sathvika²,

Rawat³

2021

IJST

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Background/Objectives: Every day millions of people visit search engines like Quora, reedit, stack overflow, etc., the demand for new intelligent techniques is growing, to help individuals find better solutions. Methods: In our proposed system, the Quora datasets were filtered using SQLite which takes one-quarter of the time taken to pre-process the same dataset using existing approaches like python functions. We used machine learning techniques namely the Random Forest, Logistic Regression, Linear SVM (Support Vector Machine) and XGBoost to analyze and identify the most suitable model. Findings: The error log loss functions (0.887, 0.521, 0.654 and 0.357) of the above machine learning techniques were analyzed and compared respectively. The performance of XGBoost is the best among the other models, hence XGBoost is the most efficient model. Conclusion/Future Scope: It is concluded that XGBoost has outperformed other machine learning techniques discussed in the study. It is also found that pre-processing using SQLite has improved the response time. In the future, we would like to apply a similar approach for various other search engines that are available like reedit, stack overflow, etc. and one could ensemble the best models of each type (linear, tree-based, and neural network).

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Section: Introductionmentioning

confidence: 99%

“…The authors clarify that the XGBoost model yields accuracy high efficiency. Sultana R. et al (4) discussed overcrowding in health care units caused by population rise over the last 10 years. Data mining models namely boosting and decision trees, originating from the tree-based approach were used for prediction activities.…”

Section: Introductionmentioning

confidence: 99%