Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: A case study of Taiwan

Wang, Kung‐Jeng; Makond, Bunjira; Wang, Kung-Min

doi:10.1016/j.compbiomed.2014.02.002

Cited by 44 publications

(33 citation statements)

References 37 publications

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“…21.7 percent of the articles in this study included brain diseases such as brain tumor, metastasis, trauma and Alzheimer (5, 9-11). Computed tomography (CT) is widely accepted as an effective method to diagnose and detect rare but clinically significant in patients suffering minor head injury or brain disease.…”

Section: Resultsmentioning

confidence: 99%

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

2016

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Introduction:Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction.Objective:This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms.Methods:PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study.Result:In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms.Discussion:This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy.Conclusion:The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

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Section: Resultsmentioning

confidence: 99%

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

2016

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“…Bayesian network specializes in the representation and reasoning technology of uncertainty knowledge and is one of popular methods in artificial intelligence, machine learning, and data mining area. Bayesian network is mainly applied in the fault diagnose, expert system, classification, planning, and learning [14][15][16][17].…”

Section: The Definition and Application Of Bayesian Networkmentioning

confidence: 99%

Comprehensive Influence Model of Preschool Children’s Personality Development Based on the Bayesian Network

Sun

Yang

2014

Abstract and Applied Analysis

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It is crucial to ascertain the comprehensive influence factors on personality for making effective cultivating plan. However, most existing literatures focus on the effect of individual factor on the personality. In order to comprehensively investigate the causal influences of preschool children’s temperament, school factors (teacher expectation and peer acceptance), and family factors (parental coparenting style, parental education value, and parental parenting style) on the personality and the probability of the dependencies among these influence factors, we constructed the influencing factor model of personality development based on the Bayesian network. The models not only reflect the influence on personality development as a whole, but also obtain the probability relationships among the factors. Compared with other influence factors including family and school factors, temperament has more effect on the personality. In addition, teacher expectation also has an important influence on the personality. The experimental results show that it is a valuable exploration to construct the Bayesian network for comprehensively investigating the causal relationships between preschool children’s personality and related influence factors. Further, these results will be helpful to the cultivation of healthy personality.

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“…Sin embargo el uso de clasificadores bayesianos, no ha sido muy usado en la detección del cáncer de mama y otros tipos de cáncer encontrándose pocos trabajos entre otros (Wang et al, 2014). Así mismo, es importante resaltar que los trabajos encontrados en la literatura, toman como base el estudio de exámenes especializados como análisis de mamografías, y otro tipo de datos.…”

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Sistema Predictivo Bayesiano para Detección del Cáncer de Mama

2018

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_____________________________________________________________________________________________________________________________________ ResumenSe propone un método predictivo para detectar el cáncer de mama, basado en las siguientes variables: edad, peso, talla, índice de masa corporal, escolaridad, estrato socioeconómico, seguridad social, fumador, cuando dejo de fumar, fumador pasivo, consume licor, cantidad de licor, herencia familiar de cáncer, edad de la menarca, menopausia, embarazos, partos, edad del primer parto, lactancia, consumo de anticonceptivos orales, cuanto años consumió anticonceptivos orales, tiempo de suspensión de anticonceptivos orales, terapia de reemplazo hormonal y presencia del gen GSTM1. Tomando como referencias pacientes de la región central de Colombia (Caldas), se definieron dos bases de datos, una de personas sin cáncer y otra de personas con cáncer. La misma base de datos de entrenamiento fue empleada para prueba. La metodología propuesta, define y entrena un sistema de clasificación bayesiano, con una base de datos de pacientes con cáncer y sin cáncer. Posteriormente, se realiza una validación del sistema con el fin de determinar el número de aciertos y errores en el reconocimiento de esta enfermedad. Como resultado, se logra un porcentaje de aciertos del 100%. Palabras clave: clasificador bayesiano; cáncer de mama; entrenamiento; detección automatizada de enfermedades Bayesian Predictive System for Detection of Breast Cancer AbstractWe propose a predictive method to detect breast cancer, based on the following variables: Age, weight, height, body mass index, schooling, socioeconomic stratum, social security, smoker, when quit smoking, passive smoker, consumption of liquor, quantity of liquor, family inheritance of cancer, age of menarche, menopause, pregnancies, age of first birth, breastfeeding, consumption of oral contraceptives, how many years of oral contraceptive use, oral contraceptive suspension time, hormone replacement therapy, and the presence of the GSTM1 gene. Taking as reference patients from the central region of Colombia (Caldas), two databases were defined, one of people without cancer and another of people with cancer. The same training database was used for testing. The proposed methodology defines and trains a Bayesian classification system, with a database of patients with cancer and without cancer. Subsequently, a system validation is performed in order to determine the number of successes and errors in the recognition of this disease. As a result, a 100% success rate is achieved.

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Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: A case study of Taiwan

Cited by 44 publications

References 37 publications

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Comprehensive Influence Model of Preschool Children’s Personality Development Based on the Bayesian Network

Sistema Predictivo Bayesiano para Detección del Cáncer de Mama

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