Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines

Chou, Shieu-Ming; Lee, Tian–Shyug; Shao, Yuehjen E.; Chen, I-Fei

doi:10.1016/j.eswa.2003.12.013

Cited by 178 publications

(80 citation statements)

References 38 publications

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“…Successful applications have been found in genomics and proteomics [11][12], cancer detection [13][14][15], heart rate variability (HRV) analysis [16], etc. For example, Chou et al used artificial neural networks to diagnose breast cancer and achieved 98 percent accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report

Jones

Jan

2014

J Rehabil Res Dev

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Abstract-Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches showed promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.Key words: artificial neural network, C4.5 decision tree, machine learning, pressure ulcer, random forest, skin blood flow, skin perfusion, spinal cord injury, support vector machine, wheelchair tilt and recline.

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Section: Introductionmentioning

confidence: 99%

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report

Jones

Jan

2014

J Rehabil Res Dev

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“…MARS has also been applied to predict the average monthly foreign exchange rates [12], to model credit scoring [13], and for data mining on breast cancer pattern [14]. In all of the cited studies, promising results have been reported where MARS has been employed either for forecasting or for data mining purposes.…”

Section: A Marsmentioning

confidence: 91%

Prediction of Recurrence in Patients with Cervical Cancer Using MARS and Classification

Chang¹,

Cheng²,

Lu³

et al. 2013

IJMLC

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“…Este sistema logra un porcentaje de aciertos en la clasificación del 98.1%. (Chou et al, 2004) Propone un modelo de clasificación de datos para identificar el cáncer de mama, integrando un modelo de regresión adaptativa a una red neuronal, obteniéndose resultados del 99.7% de efectividad en la detección de esta enfermedad. (Sahan et al, 2007) Propone un sistema inmune basado en una máquina de aprendizaje para la detección del cáncer de mama.…”

Section: Introductionunclassified

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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Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines

Cited by 178 publications

References 38 publications

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report

Prediction of Recurrence in Patients with Cervical Cancer Using MARS and Classification

Sistema Predictivo Bayesiano para Detección del Cáncer de Mama

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