NStackSenti: Evaluation of a Multi-level Approach for Detecting the Sentiment of Users

Sohan, Fahimuzzman; Rahman, Sheikh Shah Mohammad Motiur; Munna, Tahsir Ahmed; Allayear, Shaikh Muhammad; Rahman, Md. Habibur; Rahman, Md. Mushfiqur

doi:10.1007/978-981-15-1718-1_4

Cited by 3 publications

(1 citation statement)

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“…In the cross-validation approach, the data sets are divided into several equal portions; in general, 5-fold and 10-fold cross-validations are used when the data sets are equally divided into 5 and 10 portions [ 17 ]. With this approach, for each simulation, one portion of each data set is used to train the prediction model and the rest are used for validation.…”

Section: Methodsmentioning

confidence: 99%

Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Alanazi¹,

Abdou²,

Luo³

2021

JMIR Form Res

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Background Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. Objective The aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. Methods We used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. Results We found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. Conclusions The predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.

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Section: Methodsmentioning

confidence: 99%