A hybrid classification algorithm approach for breast cancer diagnosis

Abed, Baraa M.; Shaker, Khalid; Jalab, Hamid A.; Shaker, Hothefa; Mansoor, Ali Mohammed; Alwan, Ahmad Fouad; Al-Gburi, Ihsan Salman

doi:10.1109/ieacon.2016.8067390

Cited by 18 publications

(3 citation statements)

References 2 publications

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“…Sequential Minimal Optimization (SMO) is used for training a support vector classifier using polynomial or RBF kernels. It replaces all missing the values and transforms nominal attributes into binary ones [27]. A single hidden layer neural network uses exactly the same form of model as an SVM.…”

Section: Sequential Minimal Optimization (Smo)mentioning

confidence: 99%

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Application of Machine Learning Algorithms in Bioinformatics

Doulah

2019

BPOJ

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Bioinformatics is a developing domain that discourses the necessity to achieve and understand the data that in the past era was enormously produced by genomic investigation. This domain signifies the convergence of genomics, biotechnology and involves examination and understanding of data, displaying of biological occurrences, which is one of the most significant field for the study of biological schemes today and expansion of algorithms and statistics. The paper objectives at a relative study of Machine learning algorithms on a breast cancer dataset. The algorithms used for comparison of ANN, ANFIS, ADT, BN, BFT, C4.5, CART, CBR, DT, DESVM, EP, GP, GA, GRU-SVM, IBk, J48, kNN, K*, KMSVM, kNN+ICA, kNNGA, LB, LMT, LR, MDR, MLP, NNge, NB+kNN, NNANF, NB, PART, RBFNN, RF, SVM, SMO, SL, SOM, SR, SMO-SVM, ZeroR– are supervised learning algorithms used extensively for classification purposes and are chosen for their variety. The performance of those algorithms are compared by using the different performance criterions based on confusion matrix such as Recall /Sensitivity, Specificity, Precision, Accuracy, F-Score and Mathews Correlation Coefficient. Based on analysis of this data, Artificial Neural Networks (ANN) is better at classification with 99.5% accuracy and 0.9901 Mathews correlation coefficient among others classifiers.

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Section: Sequential Minimal Optimization (Smo)mentioning

confidence: 99%

“…LogitBoost with simple regression functions are base learners used for fitting the logistic models. The optimal number of LogitBoost iterations to perform is crossvalidated, which leads to automatic attribute selection [27].…”

Section: Simple Logistics (Sl)mentioning

confidence: 99%

Application of Machine Learning Algorithms in Bioinformatics

Doulah

2019

BPOJ

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“…Authors of [11] in their work, performed a comparative analysis among Radial Basis Function kernel circuitary, Multilayer Perceptron circuit nodes forest along with the canonical logistical marking regression algorithmic procedures. Abed et al [12] contemplated a categorization method of hybrid nature for cancer infected cell's interpretation and analysis. Ivankov et al [13] given an extensive comparison of some significant and practically used machine learning routines in the binate natured classification elucidation.…”

Section: A Related Workmentioning

confidence: 99%

A Machine Learning based Fine-Tuned and Stacked Model: Predictive Analysis on Cancer Dataset

Aavula¹,

Bhramaramba²

2018

ijacsa

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The earlier forecast and location of disease cells can be useful in curing the illness in medical applications. Knowledge discovery is having many significant roles in health sector, bioinformatics etc. Plenty of hidden information is available in the datasets present in the various domains like-medical information, textual analysis, image attributes exploration etc. Predictive analytics and modeling encompasses a variety of statistical methodologies from machine learning that can analyze the present along with historical facts to make the predictions about the future events. Breast cancer research already has involved with the good amount of progress in recent decade, but due to advancement in technologies, there is still some possibilities for an improvement. In this paper, the fine-tuned and stacked model procedure is presented which is experimented on standard breast cancer dataset. The obtained results show the improvement over stateof-the-art algorithms with improved performance parameters e.g. disease prediction accuracy, sensitivity and better F1 score etc.

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