Decision support system for heart disease based on support vector machine and Artificial Neural Network

Gudadhe, Mrudula; Wankhade, Kapil; Dongre, Snehlata

doi:10.1109/iccct.2010.5640377

Cited by 114 publications

(55 citation statements)

References 12 publications

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“…The network was trained and evaluated using standard 70%-30% deviation as suggested by Patricia S. Crowther [8], which is 70% from data will be used as data training and 30% from data will be use as data testing. We have used 70% of data which is 478 and 30% of data which is 20S for testing.…”

Section: Training An D Testing Datamentioning

confidence: 99%

Breast cancer detection using backpropagation neural network with comparison between different neuron

Pawar¹,

Patil²

2012

2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing

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Breast cancer is an uncontrolled growth of breast cells. Cell in the body get divide, grow and die every day. This division and growth of cell is most of the in orderly manner but when their growth is out of control. The uncontrolled growth of cell forms the lump which is called as tumor. A tumor generally of two types benign (not dangerous) or malignant (dangerous to health). The malignant tumor which develops in breast is called as breast cancer. In this paper we use backpropagation neural network for classification of breast cancer with different neuron models. It can assist doctors for taking correct decisions.

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Section: Training An D Testing Datamentioning

confidence: 99%

Breast cancer detection using backpropagation neural network with comparison between different neuron

Pawar¹,

Patil²

2012

2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing

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“…They use a nonlinear mapping to transform the original training data into a higher dimensional space, within which they search for the linear optimal separating hyperplane, or 'decision boundary', to separate the two classes. Compared with other methods such as Neural Networks, Decision Trees, Adaptive Boosting, the advantages of using SVM are [19], [14]:…”

Section: B Support Vector Machine Based Decision Support Systemmentioning

confidence: 99%

“…Therefore, besides common areas such as object recognition, handwritten digit detection, etc, SVMs have also been applied in decision support systems where prediction-based decision making is required. In [19], the authors use an SVM and an Artificial Neural Network (ANN) as bases for their heart diseases classification DSS. The SVM was used to separate the disease data into two classes, showing the presence or absence of heart diseases with 80.41% accuracy.…”

Section: B Support Vector Machine Based Decision Support Systemmentioning

confidence: 99%

A Drug Administration Decision Support System

You

Simalatsar

Widmer

et al. 2012

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops

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Abstract-Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.

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“…En [9], Gudadhe, M. et al implementan un sistema basado en máquinas de soporte vectorial (SVM) y RNAs para la predicción de enfermedad del corazón. Los resultados muestran que en general, se puede obtener mayor precisión en la clasificación empleando RNAs.…”

Section: Introductionunclassified

Heart disease predictor system based on Artificial Neural Network

Quijano

Tello

Cadena

2013

2013 Pan American Health Care Exchanges (PAHCE)

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Se presenta la implementación de un sistema para clasificar con un porcentaje de acierto, la salud de una persona que presenta una Enfermedad Cardiaca Coronaria. El sistema está basado en una técnica de inteligencia computacional (Redes Neuronales), que con base en ciertos parámetros clínicos, permite la clasificación de sujetos en dos categorías: sano o enfermo. Los resultados muestran un 94.2% de clasificación, haciendo notar que la herramienta puede asistir al personal médico en el diagnóstico de Enfermedad Cardiaca Coronaria. Abstract-An implementation of a system that classifies the health status of a subject with some success rate for CoronaryHeart Disease presence is presented. The system is based on an Soft-Computing Technique (Artificial Neural Network) supported on some clinical parameters that allow to classify patient's health between two categories: healthy or sick. A 94.2% of accuracy in a correct health status classification was acquired. This shows a tool that can assist medical personnel in Coronary Heart Disease diagnosis.

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Decision support system for heart disease based on support vector machine and Artificial Neural Network

Cited by 114 publications

References 12 publications

Breast cancer detection using backpropagation neural network with comparison between different neuron

Breast cancer detection using backpropagation neural network with comparison between different neuron

A Drug Administration Decision Support System

Heart disease predictor system based on Artificial Neural Network

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