scholarly journals Evaluation of Ensemble Machines in Breast Cancer Prediction

2020 ◽  
Author(s):  
LeenaNesamani S ◽  
NirmalaSugirthaRajini S
Keyword(s):  
Breast Cancer ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Weighted Voting ◽  
Cancer Prediction ◽  
Learning Techniques ◽  
Deadly Disease ◽  
The Individual ◽  
Better Than

Breast cancer is one of the most deadly diseases encountered among women for which the cause is not clearly defined yet. Early diagnosis may help the physicians in the treatment of this deadly disease which could turn out fatal otherwise. Machine Learning techniques are employed in the process of detecting breast cancer with greater accuracy. Individual classifiers employed in this process, predicted the disease with less accuracy when compared with ensemble models. Ensemble methods employ a group of classifiers to individually classify the data. It then combines the result of the individual classifiers using weighted voting of their predictions. Ensemble machines perform better than individual models and show improved levels in the accuracy of the prediction system. This paper examines and evaluates different ensemble machines that are used in the prediction of breast cancer and tries to identify the combinations that prove to be better than the existing ones.

scholarly journals Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age

2020 ◽  
Vol 9 (2) ◽  
pp. 380 ◽  
Author(s):  
Shangyuan Ye ◽  
Hui Zhang ◽  
Fuyan Shi ◽  
Jing Guo ◽  
Suzhen Wang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Gestational Age ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Fetal Macrosomia ◽  
Large For Gestational Age ◽  
Empirical Formulas ◽  
Learning Techniques ◽  
The Individual

Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.

scholarly journals EMG BASED DIAGNOSIS OF MYOPATHY AND NEUROPATHY USING MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol 4 (4) ◽  
pp. 38-45
Author(s):  
Anisha C. D ◽  
Arulanand N
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Domain ◽  
Neuromuscular Disorders ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Diagnostic Model ◽  
Learning Techniques ◽  
The Individual ◽  
Better Than

Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.

scholarly journals Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques

2020 ◽  
Vol 1 (5) ◽  
Author(s):  
Md. Milon Islam ◽  
Md. Rezwanul Haque ◽  
Hasib Iqbal ◽  
Md. Munirul Hasan ◽  
Mahmudul Hasan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Comparative Study ◽  
Machine Learning Techniques ◽  
Cancer Prediction ◽  
Learning Techniques

Induced Subgraph Game for Ensemble Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 1760003
Author(s):  
Hadjer Ykhlef ◽  
Djamel Bouchaffra
Keyword(s):  
Computational Cost ◽  
Ensemble Methods ◽  
Memory Storage ◽  
Machine Learning Techniques ◽  
Induced Subgraph ◽  
Ensemble Selection ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Entire Ensemble ◽  
Better Than

Ensemble methodology has proved to be one of the strongest machine learning techniques. In spite of its huge success, most ensemble methods tend to generate unnecessarily large number of classifiers, which entails an increase in memory storage, computational cost, and even a reduction in the generalization performance of the ensemble. Ensemble selection addresses these shortcomings by searching for a fraction of individual classifiers that performs as good as, or better than the entire ensemble. In this paper, we formulate ensemble selection problem as a coalitional game played on a graph. The proposed game aims at capturing two crucial concepts that affect the performance of an ensemble: accuracy and diversity. Most importantly, it ranks every classifier based on its contribution in keeping a proper balance between these two notions using Shapley value. To demonstrate the validity and the effectiveness of the proposed approach, we carried out experimental comparisons with some major selection techniques based on 35 UCI benchmark datasets. The results reveal that our approach significantly improves the original ensemble and performs better than the other methods in terms of classification accuracy, pruning ratio, and computational cost.

scholarly journals Analysis of Machine Learning Techniques for Breast Cancer Prediction

2021 ◽  
Vol 11 (1) ◽  
pp. 79-83
Author(s):  
Mrs N. Vanitha ◽  
R. Srimathi ◽  
J Haritha
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Indian Women ◽  
Cancer Prediction ◽  
Risk Of Death ◽  
Learning Techniques ◽  
Learning Technique ◽  
Sensitivity Specificity

The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women.  With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.

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