Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine

Luo, Zhaohui; Wu, Xiaoming; Guo, Sen; Ye, Binggang

doi:10.1109/icinfa.2008.4608089

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…According to the statistics published by the Iranian Ministry of Health and Medical Education, in recent years, breast cancer has been the most serious disorder in Iranian women [1]. Timely diagnosis of cancer can increase the chances of a patient's life expectancy from 56% to 86% [2]. Breast cancer, similar to other cancers, starts with a rapid and uncontrolled outgrowth and multiplication of a part of the breast tissue, which depending on its potential harm, is divided into benign and malignant types [3].…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Breast Cancer Type by Using Probabilistic Neural Network in Decision Support System

Khosravanian¹,

Ayat²

2016

IJKE

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Abstract-In this research probabilistic neural network (PNN) was used to devise a decision support system (DSS) to diagnose the type of breast cancer in patients with this disorder. The proposed method was assessed by using a reservoir of data related to patients with breast cancer, which included 699 cases stored in UCI Machine Learning Repository. To implement the network, the applications and functions in Matlab (7.12.0) were utilized. Performance indices of this system were sensitivity, specificity, and accuracy. The performance of the proposed system based on the three indices, at the network testing phase, was found to be satisfactory (sensitivity, 1; specificity, 0.98; and accuracy, 0.99).

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Section: Introductionmentioning

confidence: 99%

Diagnosing Breast Cancer Type by Using Probabilistic Neural Network in Decision Support System

Khosravanian¹,

Ayat²

2016

IJKE

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Evaluation of Health Belief Model-Based Intervention on Breast Cancer Screening Behaviors among Health Volunteers

et al. 2018

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Breast cancer is the most common cancer and the second leading cause of death among women. Regarding the lack of knowledge about the cause of breast cancer and considering the fact that all women are prone to this disease, training on methods of early diagnosis to reduce its complications is of great importance. Thus, this study aimed to determine the effect of education based on the health belief model on breast cancer screening behaviors in health volunteers of health centers in Isfahan. In this experimental study, 480 healthy volunteers were randomly divided into two groups: the case (n = 240) and control (n = 240). The training program was designed according to health belief model structures. Before the training interventional program, the Champion standard questionnaire and functional checklist were completed for both groups. A standard questionnaire was completed during three stages (before, immediately after, and 2 months after the training). The experimental group received the educational intervention during eight sessions, and the collected data was eventually analyzed using the SPSS statistical software version 16 with relevant statistical tests. Participation of all individuals in the present research was voluntary and with informed consent. The results showed that mean scores of knowledge, perceived susceptibility, severity, benefits, barriers, self-efficacy, and behavioral intention related to breast self-examination (BSE) and mammography in the intervention group significantly increased compared with those of the control group immediately after and 2 months after educational intervention. There was a significant difference between groups in BSE skill 2 months after the intervention, but there was no significant difference between the two groups in BSE behavior and mammography 2 months after the intervention. The results confirmed the efficiency and effectiveness of an educational intervention based on the health belief model on improving factors affecting breast cancer screening behaviors.

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Diagnosis of diabetes by using Adaptive SVM

Gürbüz

Kılıç

2011

2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)

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Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine

Cited by 5 publications

References 12 publications

Diagnosing Breast Cancer Type by Using Probabilistic Neural Network in Decision Support System

Diagnosing Breast Cancer Type by Using Probabilistic Neural Network in Decision Support System

Evaluation of Health Belief Model-Based Intervention on Breast Cancer Screening Behaviors among Health Volunteers

Diagnosis of diabetes by using Adaptive SVM

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