An Ensemble Machine Learning Technique for Stroke Prognosis

Duhayyim, Mesfer Al; Abbas, Sidra; Hejaili, Abdullah Al; Kryvinska, Natalia; Almadhor, Ahmad; Mohammad, Uzma Ghulam

doi:10.32604/csse.2023.037127

Cited by 5 publications

(2 citation statements)

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“…Normalization, or feature scaling, is a preprocessing approach utilized in ML to adjust different features or variables to a similar scale [ 53 , 54 , 55 ]. It is performed to ensure that no particular feature dominates the learning algorithm due to its larger magnitude or unit of measurement.…”

Section: Proposed Modelmentioning

confidence: 99%

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

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Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

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Section: Proposed Modelmentioning

confidence: 99%

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

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“…Modern medical practices depend heavily on computerized technologies, including skin cancer identification [1], [2], knee osteoarthritis [3], [4], and many further medical procedures [5], [6], [7]. The discipline of bioinformatics primarily studies drug-drug interactions (DDI) [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Drug-Drug Indicator Dataset and Ensemble Stacking Model for Detection and Classification of Drug-Drug Interaction Indicators

Abbas,

Sampedro,

Abisado

et al. 2023

IEEE Access

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Drug-drug interaction (DDI) is a significant public health issue that accounts for 30% of unanticipated clinically hazardous medication events. The past decade has seen an evolution in informaticsbased research for DDI signal identification. This paper aims to create an ensemble stacking machine learning (ML) approach capable of accurately predicting novel DDI hazard indicators. The stacking ensemble machine learning architecture for predicting the signals of drug-drug interactions is supported by one of the most reliable sources of pharmacological data, DrugBank. We scrap a large dataset that contains drug-related information, including drug types, drug names, and other aspects of drug indicators, and make it publicly available to the research community. The proposed approach includes data preprocessing, balancing through a random oversampling, label encoding and one hot encoding technique used for categorical variables, and model prediction using an ensemble stacking technique. The proposed ensemble approach uses the Gradient Boosting (GB) classifier, Adaboost, and Gaussian Naive Bayes (GNB) to classify the drug indication types. The experimental results demonstrate that the suggested approach outperforms traditional machine learning approaches regarding accuracy and efficiency. The stacking model obtained the highest accuracy of 99.0%. The test indicates that the proposed model is more effective than traditional methods for detecting drug-drug interaction signals.

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Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model

Hassan Mohamed,

Hussein Ali,

Ibrahim Taloba

et al. 2024

IEEE Access

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An Ensemble Machine Learning Technique for Stroke Prognosis

Cited by 5 publications

References 0 publications

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

A Novel Drug-Drug Indicator Dataset and Ensemble Stacking Model for Detection and Classification of Drug-Drug Interaction Indicators

Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model

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