A Comparative Study for Breast Cancer Prediction using Machine Learning and Feature Selection

Dhanya, R.; Paul, Irene Rose; Akula, Sai Sindhu; Sivakumar, M.; Nair, Jyothisha J.

doi:10.1109/iccs45141.2019.9065563

Cited by 41 publications

(8 citation statements)

References 8 publications

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“…NN's versatility due to its multiple variations such as in Zhang et al [29], Mohammed et al [30], and Higa [31], is a great choice for hyper-parameterization. Similar to SVM, LR and NN models showed robustness with BCWD in the studies of Utomo et al [16], Dhanya et al [18], Omondiagbe et al [19], Gupta and Garg [20] and Balaraman [21], making SVM, LR and NN, with rigorous examination, as the preferred classification model of the authors.…”

Section: Literature Analysismentioning

confidence: 95%

“…SVM proved to have the highest accuracy (98.1%) and Receiver Operating Characteristic (ROC) curve for both classifications. Dhanya et al[18] showed the differences in the performance of LR, naïve Bayes and random forest. This study also employed various methods of feature selection, such as sequential feature Selection, Recursive Feature Elimination (RFE), f-test and correlation.…”

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confidence: 99%

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A comparative performance of breast cancer classification using hyper-parameterized machine learning models

Mayce¹,

Hernandez²

2021

IJATEE

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Cancer is the result of the outgrowth of a clonal population of cells from bodily tissue [1]. It has been acknowledged that early oncogenesis, also known as carcinogenesis, can be determined by cell-intrinsic features. A way to determine is by illustrating these features of both cancer cells and tumors , which are the ability to provide their growth signals, disregard to growth-inhibitory signals, eluding of apoptosis (programmed cell death), unlimited replication, sustained vascularization and malignancy, invasion of tissues through basement membranes and capillary walls. Cancer is considered to have three developmental phases: initiation, promotion, and progression. In the initiation phase, genomic changes such as point mutations, amplification and gene deletion, and rearrangements in the chromosomes within the cancer cell occur. *Author for correspondence These initiated cells expand clonally and survive promoting tumor development in the promotion phase. In the progression phase, significant tumor growth and metastasis are encompassed. Accumulation of genetic lesions also plays a role in the development of cancer. Inarguably, this phenomenon is present in the Initiation phase, but it may also be present in the promotion and progression phase. In the accumulation of genetic lesions, inactivation of tumor suppressor genes also occurs which provides the cells certain properties that are needed for tumorigenesis. Many types of research have established that both cancer cells and noncancer cells with a significant amount are the recipes for the heterocellular tumor are involved in tumor development and malignancy [2, 3].In most cases, cancer derives its name after the body part in which it emanated therefore, breast cancer is the term for the unstable growth of cells that emanated in the breast tissue. The growth results in a Re search Article

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Section: Literature Analysismentioning

confidence: 95%

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confidence: 99%

A comparative performance of breast cancer classification using hyper-parameterized machine learning models

Mayce¹,

Hernandez²

2021

IJATEE

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“…Sequential forward and backward feature selection [23] and metaheuristic algorithms such as genetic algorithm [24] and particle swarm optimization [25] have been extensively used as wrapper algorithm for BC prediction. Dhanya et al [26] compared the FS and ML algorithms for BC prediction. The experiments were conducted on WBCD.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Akkur¹,

TURK²,

Eroğul³

2023

Computer Systems Science and Engineering

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Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

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“…Greater values of the f-test indicate smaller distances within the groups and greater distances between the groups. In this ANOVA feature selection method using the f-test, the features are ranked according to their f-score [26].…”

Section: B Feature Selectionmentioning

confidence: 99%

Machine Learning Algorithms for Classification Patients with Parkinson`s Disease and Hereditary Ataxias

Escamilla-Luna¹,

Wister²,

Hernández-Torruco³

2023

JCOMSS

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Neurodegenerative diseases are a group of neurological conditions characterized by the loss or destruction of neurons in the central nervous system, resulting in severe impairments and death. Researchers commonly used a two-group classification (Patients with a Neurodegenerative disease vs. healthy subjects of control). Thus, the principal purpose of this article is to distinguish between Parkinson's patients and subjects with Hereditary Ataxias using machine learning techniques. We conducted experiments using a real dataset comprising Gait characteristics derived from the inertial motion sensors of a smartphone (iPhone 5S). This investigation had 67 participants, 53 of who had Parkinson's disease and 14 of whom had Hereditary Ataxias. Methods of feature selection were applied to reduce dimensionality. In addition, five classification algorithms were constructed and assessed based on their accuracy, precision, sensitivity, and specificity. The Support Vector Machine algorithm achieved an accuracy of 92.7%, a precision of 91.1%, a sensitivity of 96.2%, and a specificity of 89.1%. These results show that the suggested technique might inspire new research issues and have a direct therapeutic impact.

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A Comparative Study for Breast Cancer Prediction using Machine Learning and Feature Selection

Cited by 41 publications

References 8 publications

A comparative performance of breast cancer classification using hyper-parameterized machine learning models

A comparative performance of breast cancer classification using hyper-parameterized machine learning models

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Machine Learning Algorithms for Classification Patients with Parkinson`s Disease and Hereditary Ataxias

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