Response to Parlange <i>et al.</i> by Y. Mualem &amp; A. Beriozkin

Mualem, Y.; Beriozkin, A.

doi:10.1111/j.1365-2389.2010.01285_2.x

Cited by 2 publications

(1 citation statement)

References 17 publications

(34 reference statements)

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“…They used LLSE as a baseline and experimented with Online Lasso and SQR as online learners [8]. Bilal, et al [9] applied machine learning algorithm for detection of initial and severe conditions of Sepsis conditions. They utilized electronic health records of the patients, for the training and the testing of model, they selected the patient's record with initial eight vital signs of Sepsis for the Sepsis prediction.…”

Section: Forward Fillingmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Sepsis Detection

Ullah

Qayyum

Hassan

et al. 2022

IJIST

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Sepsis is a very fatal disease, causing a lot of causalities all over the world, about 2, 70,000 die of Sepsis annually, thus early detection of Sepsis disease would be a remedy to prevent this disease and it would be a big relief to the family of sepsis patients. Different researchers have worked on sepsis disease detection and its prediction but still the need to have an improved model for Sepsis detection remains. We compared various machine learning algorithms for Sepsis detection and used the dataset publicly available for all the researchers at Physionet.org, the dataset contains many empty or Null values, we applied backward filling and forward filling techniques, and we calculated missing values of MAP using equation (1) which gives more precise results, we divided the 40,336 files of datasets A and B into 80% training set and 20% testing set. We applied the algorithms twice one time using vital signs and clinical values of patients and the second time using only vital signs of the patients; using vital signs only the training accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.992, 0.999, 0.981, 0.981, and 0.981 respectively, while the testing accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.987, 0.980, 0.983, 0.981, and 0.981 respectively, for Sepsis Label 0, the value of precision for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 0.99, 0.98, 0.98, 0.98, and 0.98 respectively, while the value of recall for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 1.00, 1.00, 1.00, 1.00, and 1.00 respectively; the comparison of all the above-mentioned algorithms showed that KNN leads over all the competitors regarding the accuracy, precision, and recall.

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Section: Forward Fillingmentioning

confidence: 99%