Exploring different computational approaches for effective diagnosis of breast cancer

Anuradha.,; Bharadvaja, Navneeta

doi:10.1016/j.pbiomolbio.2022.11.004

Cited by 6 publications

(7 citation statements)

References 114 publications

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“…43 ML techniques are increasingly being utilized in medical diagnostics, primarily because classification and identification systems are more efficient in assisting medical professionals in disease diagnosis. 3 Thorough evaluation of AI systems paves the way for clinical studies aimed at enhancing the accuracy and effectiveness of BC diagnosis.…”

Section: The Approach Usedmentioning

confidence: 99%

“…The use of ML algorithms and various AI‐based models is rapidly advancing in various healthcare sectors and research institutions, 42 particularly in cancer research, with enormous practical applications 43 . ML techniques are increasingly being utilized in medical diagnostics, primarily because classification and identification systems are more efficient in assisting medical professionals in disease diagnosis 3 . Thorough evaluation of AI systems paves the way for clinical studies aimed at enhancing the accuracy and effectiveness of BC diagnosis.…”

Section: The Approach Usedmentioning

confidence: 99%

“…1 There are several risk factors associated with the development of BC, including age, obesity, heavy alcohol consumption, a family history of BC, a history of radiation exposure, cigarette smoking, and postmenopausal hormone therapy. 3 The diagnosis and treatment of this disease state have improved over the years with the recent advances in artificial intelligence (AI) which have comprehensively enhanced survival. However, the need to distinctly know the strategy to apply in managing the situation to avoid its recurrence has lacked adequate elucidation and focus in recent times.…”

Section: Introductionmentioning

confidence: 99%

“…BC is a disease that poses a high risk to women of all age groups 1 . There are several risk factors associated with the development of BC, including age, obesity, heavy alcohol consumption, a family history of BC, a history of radiation exposure, cigarette smoking, and postmenopausal hormone therapy 3 . The diagnosis and treatment of this disease state have improved over the years with the recent advances in artificial intelligence (AI) which have comprehensively enhanced survival.…”

Section: Introductionmentioning

confidence: 99%

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Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Ejiyi,

Qin,

Monday

et al. 2023

BioFactors

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Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)‐augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine‐learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.

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Section: The Approach Usedmentioning

confidence: 99%

Section: The Approach Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Ejiyi,

Qin,

Monday

et al. 2023

BioFactors

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“…One of the leading causes of death in women around the world has been recognized as BC. Recent studies have shown that earlier detection is crucial for utilizing effective treatments and lowering the mortality rate among women from BC [2]. Globally, BC accounts for 15% of all malignancies that affect women [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

Reshan,

Amin,

Zeb

et al. 2023

Life

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Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.

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Multimodality Imaging Data-Driven Prediction Architecture for Breast Cancer

Mir,

Khan,

Sasaki

et al. 2023

2023 IEEE World Conference on Applied Intelligence and Computing (AIC)

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Exploring different computational approaches for effective diagnosis of breast cancer

Cited by 6 publications

References 114 publications

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

Multimodality Imaging Data-Driven Prediction Architecture for Breast Cancer

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