Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Singh, Law Kumar; Khanna, Munish; Singh, Rekha

doi:10.1016/j.advengsoft.2022.103338

Cited by 40 publications

(20 citation statements)

References 67 publications

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“…This growth can be primarily attributed to advancements in the physical devices utilized in this domain and the remarkable increase in processing power of computer systems. Various areas have benefited from these advances in artificial intelligence, including the medical, automotive, aerospace, and educational sectors [1][2][3][4][5][6][7][8]. Additionally, artificial intelligence has played a vital role in the development of smart cities [9,10], among other fields.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

García-Domínguez

Galván-Tejada

Magallanes‐Quintanar

et al. 2023

Journal of Diabetes Research

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The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

García-Domínguez

Galván-Tejada

Magallanes‐Quintanar

et al. 2023

Journal of Diabetes Research

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“…Singh, L.K. et al [30] proposed a unique feature selection method based on eagle strategy optimization (ESO), the gravitational search optimization (GSO) algorithm, and their hybrid algorithm, which could select the fewest features to achieve the highest accuracy. Experimental results showed that the proposed method achieved great results on the WDBC dataset with an accuracy of 0.9896.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

Chen,

Wang,

Zhou

et al. 2023

Applied Sciences

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In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget ε is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.

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“…A remarkable amount of research has been done in the area of building models that assist in predicting different types of diseases and health related problems especially those related to Breast Cancer, using different machine learning algorithms. This section presents some of the works done in this area and their various outcomes [6].…”

Section: Introductionmentioning

confidence: 99%

“…Results show that KNN gives the highest accuracy (97.51%) with the lowest error rate then NB classifier (96.19 %). Also Wisconsin Diagnostic Breast Cancer (WDBC) was employed by [6,[8][9][10][11], for their classification of breast cancer using machine learning techniques with different classifiers such as SVM, KNN, DT, ANN etc and a hybrid technique such as SVM and Computer Aided Design (CAD) and more.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Some Selected Breast Cancer Classification Algorithms in Nigeria

Suleiman

Eviwiekpaefe

Yakubu³

et al. 2022

J Biochem Microbiol Biotechnol

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Breast Cancer (BC) is a prevalent disease that affects mostly women in the world. According to the World Health Organization (WHO), BC represent about 25 percent of all cancers in women with 685 000 deaths in 2020. An early detection of this disease can greatly increase the chances of taking the right decision on a successful treatment plan. This resulted in the need of new research avenues most especially in a country like Nigeria where there is low awareness of the disease and late presentation of BC by patients is normal. To achieve this, Support Vector Machine (SVM), KN Neighbor (KNN) and Decision Tree (DT) was used on a local dataset obtained from Ahmadu Bello University Teaching Hospital Zaria to provide some effective diagnostic capabilities. The dataset was classified into three classes (Benign, Pre-malign and Malign) and the SVM obtained a good classification accuracy of (99.2%). Late presentation of breast cancer is normal because of low awareness of the disease in the country therefore more awareness of the disease is highly recommended and women above the age of 34 years should always go for the breast cancer screening at least once a year with or without sign, sickness or symptoms.

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Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Cited by 40 publications

References 67 publications

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

Evaluation of Some Selected Breast Cancer Classification Algorithms in Nigeria

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