Using Stacking methods based Genetic Algorithm to predict the time between symptom onset and hospital arrival in stroke patients and its related factors

Amani, Firouz; Abdollahi, Jafar; Mohammadnia, Alireza; Amani, Paniz; Fattahzadeh-Ardalani, Ghasem

doi:10.18502/jbe.v8i1.10401

Cited by 4 publications

(2 citation statements)

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“…( (2) TP Sensitivity TP FN    Specificity: A metric called sensitivity measures how well the model predicts the true positive outcome for each available category. A parameter known as specificity measures how well the model predicts the actual negative for each category provided [14,15].…”

Section: Evaluation Criteriamentioning

confidence: 99%

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Diabetes Data Classification using Deep Learning Approach and Feature Selection based on Genetic

Abdollahi

Nouri-Moghaddam

Mirzaei

2023

Preprint

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The area of deep learning research (DL) has seen significant growth in recent years and has shown incredible promise for AI in medical applications. In this approach, we have proposed a deep neural network framework for the categorization of feature-based diabetes data. The dataset is classified using the SoftMax layer after features are collected from it using the wrapper approach. Additionally, the network is adjusted through supervised grid research with the training dataset. However, due to the risk of misdiagnosis in a medical context, we evaluated our model using measures such as precision, recall, specificity, and F1 score and obtained superior results. The proposed architecture has been tested using Diabetes 130-US hospital data, which consists of 100,000 patient files with an average of 55 attributes. As well, five machine learning algorithms—Nave Bayes, Random Forests, Decision Trees, Support vector machines, and Ensemble learning—are used to compare results from experiments. The classification accuracy of the ML algorithm was 86%. This research suggests a Deep Learning system based on feature selection for classifying diabetes data. This method has been tested on UCI machine learning data and has outperformed several other classification techniques, reaching 85.61% accuracy in this study.

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Section: Evaluation Criteriamentioning

confidence: 99%

“…Therefore, artificial intelligence technology is a suitable research area for us. whole-slide pathology [8], X-ray [9], diabetes [1,2], breast cancer [10], heart [11], time series [12], medicinal plants [13], stock exchange [14], Strokes [15] and others make good use of machine learning and deep learning.…”

Section: Introductionmentioning

confidence: 99%