A new classifier for breast cancer detection based on Naïve Bayesian

Karabatak, Murat

doi:10.1016/j.measurement.2015.04.028

Cited by 149 publications

(62 citation statements)

References 21 publications

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“…Some literatures, such as [5,13], supposed features of breast tumor were independent. According to this assumption, we trained and tested Naïve Bayes (NB) model also based on 5-fold cross validation.…”

Section: Resultsmentioning

confidence: 99%

Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

Ding¹,

Cao²,

Zhai³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract:The paper employed Bayesian network (BN) modelling approach to discover causal dependencies among different data features of Breast Cancer Wisconsin Dataset (BCWD) derived from openly sourced UCI repository. K2 learning algorithm and k-fold cross validation were used to construct and optimize BN structure. Compared to Naïve Bayes (NB), the obtained BN presented better performance for breast cancer diagnosis based on fine needle aspiration cytology (FNAC) examination. It also showed that, among the available features, bare nuclei most strongly influences diagnosis due to the highest strength of the influence (0.806), followed by uniformity of cell size, then normal nucleoli. The discovered causal dependencies among data features could provide clinicians to make an accurate decision for breast cancer diagnosis, especially when some features might be missing for specific patients. The approach can be potentially applied to other disease diagnosis.

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Section: Resultsmentioning

confidence: 99%

Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

Ding¹,

Cao²,

Zhai³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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show abstract

“…Comparisons between the proposed method and other works were performed and the results favor the proposed framework with 97.42% classification accuracy. In [16], a new weighted Naïve Bayes classifier is presented for breast cancer detection. Several experiments were completed to evaluate the performance of the proposed technique.…”

Section: Medical Applicationsmentioning

confidence: 99%

Hybrid Multistage Fuzzy Clustering System for Medical Data Classification

Abdullah

Al-Anzi

Al-Sharhan

2018

EasyChair Preprints

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Abstract-Due to the rapid development in technology nowadays, massive amount of data are available. In medicine, decision making is entirely based on the hidden information in these massive data. For that reason, data mining and machine learning technologies provide powerful tools for knowledge discovery within data. Two main techniques are used interchangeably: clustering and classification. In machine learning, clustering is an unsupervised learning technique while classification is a supervised learning method. These techniques are capable of extracting useful patterns and information which aid the process of data analysis and clinical decisions. This research presents a recent study of these techniques in the medical field during the past five years. Moreover, this paper proposes a hybrid multistage fuzzy clustering system applied to medical data classification. In the proposed system, two fuzzy clustering algorithms specifically FCM and GK were initially employed to obtain the membership values. These weights are then used in the second stage of the system as additional informative features to improve the classification process completed by SVM algorithm. Wisconsin Breast Cancer dataset, real-world application, obtained from UCI were used in the experiments. The results of the experiments show that the additional weights further improve the classification accuracy with 99.06% and 100% sensitivity.

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“…(Kim et al, 2014) Se realiza un algoritmo de aprendizaje evolucionario en la detección del cáncer de mama y mieloma múltiple, el cual es optimizado por medio de un secuencia bayesiana simple, con muy buenos resultados (Calderon et al, 2014) Se propone una metodología para la detección de cáncer de mama, con frecuencias no ionizantes. (Karabatak et al, 2015) Emplea un clasificador bayesiano ponderado en la detección del cáncer de mama. con resultados superiores al 98% (Liu et al, 2015) Propone un sistema de detección de cáncer de mama sobre las mamografías.…”

Section: Autores Contribuciónunclassified

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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A new classifier for breast cancer detection based on Naïve Bayesian

Cited by 149 publications

References 21 publications

Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

Hybrid Multistage Fuzzy Clustering System for Medical Data Classification

Sistema Predictivo Bayesiano para Detección del Cáncer de Mama

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