Breast Cancer Diagnosis Using Optimized Machine Learning Algorithms

Bensaoucha, Saddam

doi:10.1109/icrami52622.2021.9585977

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Precision demonstrates a model's performance based on the correct diagnosis, which represents the percentage of really positive samples among positive predicted samples [29]. The recall is the ratio of the correctly predicted positive samples to all predicted positive samples in the original class, which is also called the rate of true positive [10], [15]. F1-score is the harmonic average of the pre and rec index, which is always less than the value of accuracy as it is calculated with precision and recall value [10], [17].…”

Section: Performance Matricesmentioning

confidence: 99%

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Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Hridoy,

Arni,

Ghosh

et al. 2024

IJECE

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Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

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Section: Performance Matricesmentioning

confidence: 99%

“…Using RFE, NN, SVM, and NB obtained 98.24%, 96.47%, and 91.18% accuracy, respectively. Bensaoucha [15] used optimized ML models for BC diagnosis, and the prediction performance of DT, NB, SVM, k-NN, and MLP was evaluated. BO and GS methods were used for optimizing ML classifiers, both SVM and MLP obtained 96.52% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Hridoy,

Arni,

Ghosh

et al. 2024

IJECE

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“…Extra Tree Classifier algorithms was the best classification method with accuracy of 96,2%. Bensaoucha et al [31] compared several classification algorithms for BC prediction. The hyperparameters of the algorithms were determined using BO approach.…”

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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Prediction of Chronic Venous Insufficiency Condition Using Infrared Thermography and Machine Learning

Krishnan,

Muthu

2024

Lecture Notes in Networks and Systems

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Breast Cancer Diagnosis Using Optimized Machine Learning Algorithms

Cited by 6 publications

References 14 publications

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Prediction of Chronic Venous Insufficiency Condition Using Infrared Thermography and Machine Learning

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