Applying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study

Atashi, Amir; Sohrabi, Soolmaz; Dadashi, Ali

doi:10.30699/acadpub.mci.2.2.8

Cited by 6 publications

(1 citation statement)

References 9 publications

(15 reference statements)

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“…The findings of a study by Atashi et al [ 65 ] conducted on a database with 4004 records, including demographic risk factors showed the higher performance of the neural network (sensitivity= %80.9, specificity= %99.8, accuracy= %62.8) compared to other approaches, such as C5.0. Mosayebi et al study [ 66 ] was conducted on a database with 5471 records, including demographic and laboratory features reported for C.50 (accuracy 82%, sensitivity 86%.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Breast Cancer using Machine Learning Approaches

Rabiei

2022

J Biomed Phys Eng

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Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

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Section: Discussionmentioning

confidence: 99%

Prediction of Breast Cancer using Machine Learning Approaches

Rabiei

2022

J Biomed Phys Eng

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Horizontal Collaboration in Crisis Management: An Experimental Study of the Duty Officer Function in Three Public Agencies

Deverell¹,

Alvinius²,

Hede³

2019

Risk Hazard & Crisis Pub Pol

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Lately, scholars have been engaging in studies on the crossroads between collaborative public management and crisis management, but our knowledge on how organizations work together with interorganizational goals in times of crisis or in relation to the threat that crises pose, is still limited. This study aims to illuminate how interagency horizontal collaboration plays out in practice. An interview study with twenty‐three Swedish duty officers in three organizations with critical tasks in the institutional crisis management system is carried out to unearth impediments to interagency collaboration that emerge when organizations from different policy subsystems engage in crisis collaboration. Empirically, the study contributes with new knowledge on how a crucial function in crisis management carries out work and deals with challenges. Theoretically, it contributes to the literature on crisis management collaboration by shedding light on processes of horizontal collaboration in the area of crisis management.

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Comparative Analysis on Breast Cancer Prediction Using Machine Learning Techniques

Raj

Pallavi

2023

Advances in Intelligent Systems and Computing

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Applying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study

Cited by 6 publications

References 9 publications

Prediction of Breast Cancer using Machine Learning Approaches

Prediction of Breast Cancer using Machine Learning Approaches

Horizontal Collaboration in Crisis Management: An Experimental Study of the Duty Officer Function in Three Public Agencies

Comparative Analysis on Breast Cancer Prediction Using Machine Learning Techniques

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