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Prediction of Sepsis Disease Using Random Search to Optimize Hyperparameter Tuning Based on Lazy Predict Model

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Sepsis is a severe infection-related host response that is linked with high mortality, morbidity and healthcare expenditures. Its treatment must be done quickly since each hour of delay increases death owing to irreparable organ damage. In the meantime, notwithstanding decades of clinical study, there are no reliable biomarkers for sepsis. As a result, early detection of sepsis using the abundance of high-resolution intensive care data has become a difficult task. There are also certain machine learning (ML) grounded models that could cut death rates, although their accuracy isn't always reliable. This research offers a lazy predict (LP) model of ML algorithm for identifying and forecasting sepsis in intensive care unit (ICU) patients. LP model is one of the finest Python packages for semi-automating ML tasks. It generates a large number of basic models with little code and aids in determining which models function best without any parameter adjusting. This study describes various models such as XGB classifier, LGBM classifier, extra tree classifier, random forest classifier, bagging classifier and decision tree classifier are based on vital signs and clinical laboratory results and are simulated using information taken from an intensive care unit patient's database. Then, after getting evaluation of all the models, XGB classifier attains higher accuracy of 0.98 which is the best fit to use the LP library compare to other ML model. Moreover, this empirical study proposed hyperparameter tuning based on random search is applied to XGB classifier used to train a model. To overcome the classification challenge, this research work introduces a Lazy Classifier. Hence, the best score across all searched parameters using random search to optimize hyperparameter tuning which attains 0.99 for lazy predict with XGB algorithm.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India

    E. Laxmi Lydia

  2. Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia

    Sara A. Althubiti

  3. Department of Electronics and Instrumentation Engineering, V.R. Siddhartha Engineering College, Vijayawada, 520007, India

    C. S. S. Anupama

  4. Department of Computer Science and Engineering, GITAM School of Technology, Vishakhapatnam Campus, GITAM (Deemed to be a University), Visakhapatnam, India

    Kollati Vijaya Kumar

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  1. E. Laxmi Lydia
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  2. Sara A. Althubiti
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  3. C. S. S. Anupama
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  4. Kollati Vijaya Kumar
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Correspondence to E. Laxmi Lydia .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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Lydia, E.L., Althubiti, S.A., Anupama, C.S.S., Kumar, K.V. (2023). Prediction of Sepsis Disease Using Random Search to Optimize Hyperparameter Tuning Based on Lazy Predict Model. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_31

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