Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Hsieh, Shu‐Kai; Hsieh, Shuchen; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei

doi:10.1007/s10916-011-9762-6

Cited by 68 publications

(28 citation statements)

References 9 publications

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“…First the individual machine learning classification methods are trained, tested and tuned on the database [28,29]. In this work we picked 4 different machine learning methods to use as individual ML methods therefore n = 4: Disjunctive Normal Form (DNF) rule based method (CN2 learner) [5,28], Decision Tree [28,29], Support Vector Machines (SVM) [28,29] and Naïve Bayes [28,29,30]. Each ML classification method goes through training, testing and tuning phases.…”

Section: Adaptive Methods and Stopping Criteriamentioning

confidence: 99%

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A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

Deng

Perkowski

2015

2015 IEEE International Symposium on Multiple-Valued Logic

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A novel Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) machine learning (ML) method was developed for breast cancer detection. It was constructed using three individual ML methods based on Multiple-Valued Logic: Disjunctive Normal Form (DNF) rule based method, Decision Trees, Naïve Bayes, and one method based on continuous representation: Support Vector Machines (SVM). Results were compared with other methods and show that the WHAVE method accuracy was noticeably higher than the individual ML methods tested. This paper demonstrates that the WHAVE method proposed outperforms all methods researched, and shows the advantage of using WHAVE method for ML in breast cancer detection.

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Section: Adaptive Methods and Stopping Criteriamentioning

confidence: 99%

“…This was the highest accuracy out of all the previous research methods that we found in literature. An ensemble method was tested in 2011 combining the methods Neural Fuzzy, K-Nearest Neighbors, and Quadratic Classifier, resulting in 97.14% accuracy [5]. This ensemble method did not use weights or have a hierarchical system of testing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

Deng

Perkowski

2015

2015 IEEE International Symposium on Multiple-Valued Logic

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“…It also helps to reduce variance and avoid overfitting, and is generally applied to decision tree methods but can be used with any type of method. The bagging method proceeds according to the procedure depicted in Figure 8 [22,23]. In general, in case of categorical data, the predictor is counted by voting, and in case of continuous data, it is counted by the average.…”

Section: Baggingmentioning

confidence: 99%

“…One of the ensemble classification methods, the bagging method, is another name for bootstrap aggregation. It is generally classified based on a decision tree, and it is a method for changing the number of boosts to improve classification accuracy [22,23]. The k-nearest neighbor (kNN) method is a nonparametric method that was used in statistical applications in the early 1970s.…”

Section: Introductionmentioning

confidence: 99%

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

et al. 2020

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The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.

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“…Hsieh et al [53] have developed and ensemble machine learning model for diagnosing breast cancer. In this model, information-gain has been adopted for feature selection.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Nalluri

Kannan

Manisha

et al. 2017

Journal of Healthcare Engineering

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Cited by 68 publications

References 9 publications

A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

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