Cerebral Infarction Classification Using Genetic Algorithm Neural Network and Stochastic Neural Network

Wirasati, Ilsya; Rustam, Zuherman; Aurellia, Jane Eva

doi:10.1007/978-3-030-90633-7_42

Cited by 1 publication

(5 citation statements)

References 16 publications

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“…Some related works found accuracy higher than 90% (Abugabah et al, 2022;Apostolopoulos & Mpesiana, 2020;Chen et al, 2017;Chumachenko et al, 2022;Delen, 2009;Iwendi et al, 2020;Peng et al, 2022;Raghavendra et al, 2023;Singh et al, 2021;Wirasati et al, 2022).…”

Section: Overall Discussionmentioning

confidence: 98%

“…Chen et al (2017), Wirasati et al (2022), and Peng et al (2022). explored artificial neural networks to predict the risk of a chronic disease of cerebral infarction.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed model achieved high accuracy of 99.33% and 99.13%, respectively, for the right and left hemispheres of the brain. Chen et al (2017), Wirasati et al (2022), andPeng et al (2022). explored artificial neural networks to predict the risk of a chronic disease of cerebral infarction.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the authors claim that, for some diseases, prediction requires a greater diversity of data, such as data obtained from texts annotated during the doctor's appointments. Wirasati et al (2022) used genetic algorithm neural network (GA-NN) and stochastic neural network (S-NN) to classify cerebral infraction data received from Cipto Mangunkusumo Hospital (Indonesia), having an accuracy by up 95.23% (S-NN) and 92.85% (GA-NN).…”

Section: Related Workmentioning

confidence: 99%

“…Wirasati et al (2022) used genetic algorithm neural network (GA‐NN) and stochastic neural network (S‐NN) to classify cerebral infraction data received from Cipto Mangunkusumo Hospital (Indonesia), having an accuracy by up 95.23% (S‐NN) and 92.85% (GA‐NN).…”

Section: Related Workmentioning

confidence: 99%

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Automated and intelligent system for World Health Organization data forecasting

Soares,

de Freitas

2023

Expert Systems

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Technological advances and social transformations have enabled the circulation of a large amount of data in the health area. Analyzing this data becomes more critical and more challenging as the volume of data increases. An alternative to performing this analysis is to use data analysis techniques to process input data sets and build consistent databases for input to machine learning algorithms. Thus, it can forecast future scenarios and collaborate with knowledge discovery. In this context, this work aims to develop a parameterizable system with automated decisions capable of collecting and analyzing many indicators provided by the World Health Organization (WHO). After these analysis steps, the system applies machine learning algorithms for predictions of different indicators to process information automatically, finding different knowledge discovery scenarios. Thus, the contribution of this article is an automated and intelligent system for WHO data forecasting. The efficiency of the system's choices and forecasts was proven with experiments in five different areas of health, obtaining assertiveness by up to 99.92%, root‐mean‐square error (RMSE) by up to 0.0286, and Kling‐Gupta Efficiency (KGE) by up to 0.9988, hitting even the most complex cases, as shown in the confusion matrices. Finally, three case studies were carried out to expand the studies and present the potential of the system in different contexts: anemia in children, age‐standardized suicide rates in men, and number of road traffic deaths.

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Section: Overall Discussionmentioning

confidence: 98%

“…Chen et al (2017), Wirasati et al (2022), and Peng et al (2022). explored artificial neural networks to predict the risk of a chronic disease of cerebral infarction.…”

Section: Related Workmentioning

confidence: 99%