The Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosis

Ateş, İbrahim; Bilgin, Turgay Tugay

doi:10.18521/ktd.912462

Cited by 5 publications

(10 citation statements)

References 21 publications

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“…In this study, unlike previous studies, the step of detection and exclusion of outliers was added to the data set. When the table is examined, it is seen that the results of the studies with feature selection [13,[16][17][22][23][24] are considerably higher than the results of the studies without feature selection [14,[16][17][18][19][20][21][25][26]. PCA, KPCA, etc.…”

Section: Resultsmentioning

confidence: 99%

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Breast Cancer Diagnosis with Machine Learning Techniques

DOĞAN

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Tanyıldızı

et al. 2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

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Section: Resultsmentioning

confidence: 99%

“…Ateş et.al. [19] using the WBCD data set, achieved 96.5%, 92.4%, and 96.5% classification accuracy rates as a result of the NB, DT, and MLP methods. Selvi et.al.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Diagnosis with Machine Learning Techniques

DOĞAN

Tatar

Tanyıldızı

et al. 2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…The importance of blockchain technology has been emphasized against the difficulties experienced in associating digital identity and real identity with a secure method [29]. By using the artificial neural network method, 96.5% success was achieved in the diagnosis of breast cancer [30]. Similar studies are carried out on many different methods.…”

Section: Literaturementioning

confidence: 99%

A Study on Remote Detection of Turkey Digital Identity Card Hologram Element

2024

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Technological advances and the pandemic have caused an explosion in demand for remote services. In critical processes such as banking, insurance and notary public, the service to be provided remotely has to be provided as quickly and safely as the service provided in presence. In such sensitive processes, the identity card authenticity detection must be done accurately and securely. One of the techniques used in accuracy detection is the use of mobile devices that support contactless chip technology. However, the number of mobile devices using this method is limited today. This situation hinders the effective and widespread use of remotely provided services and resources. Therefore, a different solution method was needed that would be an alternative to contactless chip technology, remove the obstacles to widespread use and reduce incidents such as counterfeiting. In this study, it was investigated whether the hologram, which is one of the security elements in the identity, can be used in determining the accuracy of Turkey's digital identity cards. For this purpose, a special function and program has been developed on the MATLAB application by using 2-D neighborhood methods to detect the crescent on the hologram. 227 digital ID cards were first evaluated on a mobile device over certain threshold values such as color level and similarity ratio. Then, each card used in the test was examined one by one with the naked eye. Approximately 99.56% similar results were obtained between the test findings made over the application and the findings obtained after the controls made with the naked eye. It has been concluded that the secure hologram image, one of the security elements on the digital ID card with this high success rate, can be used as computer aided in remote services. With this solution method, it has contributed to overcome an important obstacle in front of services that are thought to be provided remotely in many fields such as banks, notaries and education.

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“…For example, [50] utilized SVM classifier in their studies to achieve an Acc of 97.47%. Similarly, in [51], a single classifier was utilized, and a classification performance of 96.5% was achieved in terms of Acc when using NB and ANN classifiers. Amrane et al [52] used KNN and attained an Acc of 97.5%.…”

Section: Comparison With Literaturementioning

confidence: 99%

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Hesham¹,

El-Rashidy²,

Rezk³

et al. 2022

IJACSA

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Breast cancer (BC) is considered the most common cancer among women and the major reason for the increased death rate. This condition begins in breast cells and may spread to the rest of the body tissues. The early detection and prediction of BC can help in saving a patient's life. In the last decades, machine learning (ML) has played a significant role in the development of models that can be used to detect and predict various diseases at an early stage, which can greatly increase the survival rate of patients. The importance of ML Classification is attributed to its capability to learn from previous datasets, detects patterns that are difficult to comprehend in massive datasets, predicts a categorical variable within a predefined example and provide accurate results within a short amount of time. Feature selection (FS) method was used to reduce the data dimensionality and choose the optimal feature set. In this paper, we proposed a stacking ensemble model that can differentiate between malignant and benign BC cells. A total of 25 different experiments have been conducted using several classifiers, including logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), K-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM). In addition to several ensembles, the classifiers included random forest (RF), bagging, AdaBoost, voting, and stacking. The results indicate that our ensemble model outperformed other state-of-the-art models in terms of accuracy (98.6%), precision (89.7%), recall, and F1 score (93.33%). The result shows that the ensemble methods with FS have a high improvement of classification accuracy rather than a single method in detecting BC accurately.

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The Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosis

Cited by 5 publications

References 21 publications

Breast Cancer Diagnosis with Machine Learning Techniques

Breast Cancer Diagnosis with Machine Learning Techniques

A Study on Remote Detection of Turkey Digital Identity Card Hologram Element

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

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