Prediction of fatty liver disease using machine learning algorithms

Wu, Chieh-Chen; Yeh, Wei‐Ting; Hsu, Wen-Ding; Islam, Mohaimenul; Nguyễn, Phụng Anh; Poly, Tahmina Nasrin; Wang, Yao Chin; Yang, Hsuan-Chia; Li, Yu-Chuan

doi:10.1016/j.cmpb.2018.12.032

Cited by 236 publications

(126 citation statements)

References 28 publications

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“…With this in mind, several studies has been conducted to increase the sensitivity and specificity by proposing better classification characterized algorithms to reduce the misclassification. Some of them are successfully producing higher sensitivity and specificity algorithms, especially algorithms that related to ASD classification and other medical fields [11][12][13][14][15]. Previous studies conducted by Komisicky et al [11], autistic detection using SVM, Naïve Bayes, decision tree variants, RF.…”

Section: Introductionmentioning

confidence: 99%

“…In research by [15], the authors compared to machine learning algorithms: RF, Logistic Regression (LR), ANN (MLP) for predictions of fatty liver disease. The results of optimal sensitivity and specificity is generated by RF.…”

Section: Introductionmentioning

confidence: 99%

“…However, these previous studies did not yet apply their algorithms on ASD datasets in this study. So, this study will use SVM, RF, and ANN to acquire a higher sensitivity and specificity algorithms using the same algorithms as the [11][12][13][14][15] . The aim of this study is to increase the level of sensitivity and specificity in previous studies [10] for ASD classification on 3 AQ-Screening (AQ-Child, AQ-Adolescent, and AQ-Adult).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm for improving performance on 3 AQ-screening classification

Pratama

Hartanto

Setiawan

2019

CST

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Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm for improving performance on 3 AQ-screening classification

Pratama

Hartanto

Setiawan

2019

CST

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“…Kannan y Vasanthi (2019) utilizan algoritmos de aprendizaje automático con curva RoC para predecir y diagnosticar la enfermedad cardíaca en india, comparan la precisión de cuatro algoritmos de aprendizaje automático mediante los 14 atributos de los conjuntos de datos cardíacos UCi. Un trabajo similar, pero aplicado a la enfermedad del hígado graso, considerando 577 pacientes (Wu, 2019).…”

Section: Algoritmos Utilizadosunclassified

La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena

Ochoa¹,

Lis-Gutiérrez²,

Nieto³

et al. 2019

Diálogos Sobre Investigación: Avances Científicos Konrad Lorenz

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“…Indeed, ML techniques are evolving towards solving real health problems. Indeed, among these problems we nd the detection and the prediction of diseases such as diabetes, heart disease, cancer, liver disease, and brain diseases [2][3][4][5][6]. The ML techniques usually include neural network, support vector machine, Naïve Bayes, Decision Tree, and genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison approach of different disease prediction studies using quantitative index

ElHamdaoui¹,

Boujraf²

2020

Preprint

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Background and aim Comparing databases performances is quite challenging, especially they are presenting different characteristics including the quantitative and quantitative aspects acquired. The primary aim of this paper is to introduce a quantitative method for performance comparison of prediction of diseases studies using measurable parameters. Results The proposed method focuses on the following parameters: the accuracy and the number of attributes, to compute the performance index. We outline the major obstacles in terms of the construction of a comparative reliable approach, and a demonstrative case regarding the prediction of heart disease is given to validate the effectiveness of the proposed method. Conclusions Comparing performance of databases used in computer aided diagnosis should be based on objective quantitative basis, inhere presented index allows to achieve this goal, and overcome the variability found through databases. The presented methods should constitute a major tool for normalizing the comparison of databases performances, hence allowing more transparent and better evidencing databases qualities.

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Prediction of fatty liver disease using machine learning algorithms

Cited by 236 publications

References 28 publications

Machine learning algorithm for improving performance on 3 AQ-screening classification

Machine learning algorithm for improving performance on 3 AQ-screening classification

La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena

Performance comparison approach of different disease prediction studies using quantitative index

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