A Hybrid Model to Support the Early Diagnosis of Breast Cancer

Carvalho, Davi; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calí­ope Dantas

doi:10.1016/j.procs.2016.07.112

Cited by 24 publications

(12 citation statements)

References 9 publications

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“…Diagnostic interventions are the first interventions after a disorder has been detected. As shown in Figure , two papers (Carvalho, Pinheiro, & Pinheiro, ; Nunes et al, ) used MCDA for diagnostic decisions. Treatment interventions, which target individuals who suffer from diagnosable diseases, are the final step and are used to reduce illnesses' symptoms/effects or to eliminate them.…”

Section: Resultsmentioning

confidence: 99%

“…One study (Koch & Rowell, ) was concerned with treatment interventions and aimed at assessing an organ transplant allocation process. Finally, the last study (Carvalho et al, ) was performed to support early diagnosis of breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…The number of stakeholders participating in the case studies varied greatly, from three to more than 100. It is important to mention that the study supporting the diagnosis of breast cancer (Carvalho et al, ) did not involve any stakeholder. In that study, the model was built according to a hospital database.…”

Section: Resultsmentioning

confidence: 99%

“…DCE was used to represent the relative priority (i.e., priority scores) of each scenario (Marsh et al, ) or intervention (Baltussen, Stolk, Chisholm, & Aikins, ) based on their characteristics/criteria. Authors also used other tools, such as swing weights ( n = 6), fuzzy sets ( n = 6), the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH; Carvalho et al, , Nunes et al, , Longaray et al, ), and the Delphi method (Poulin et al, ; Ivlev et al, ; Cangialose et al, ; Wang et al, ).…”

Section: Resultsmentioning

confidence: 99%

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Healthcare decision‐making applications using multicriteria decision analysis: A scoping review

Glaize

Duenas²,

Martinelly

et al. 2019

Multi Criteria Decision Anal

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The objective of this study is to review applications of multicriteria decision analysis (MCDA) methods to provide structure for and practical insights on how MCDA methods are applied in different healthcare areas. Four databases (Embase, Medline, Web of Science, and PubMed) were searched from 1980 to February 7, 2018. A scoping review was performed to outline the most frequently applied MCDA methods, in which only case studies were considered, as the objective was to assess the applications of MCDA methods. Seventy case studies were selected. The review showed that the first real-life application was published in 1990. With regard to areas of application, three aspects were analysed: type of health services (public or private), type of intervention (preventive, diagnostic, or treatment), and healthcare area (policymaking; resource allocation; health technology assessment; pharmaceutics; and hospital, clinical, or patient level). Data revealed that MCDA was principally used in public health services (n = 59). Three types of interventions were identified: prevention (n = 36), diagnosis (n = 32), and treatment (n = 2). An MCDA process of application was then followed to provide practical insights. During the problem-structuring phase, results showed that processes for selecting stakeholders and criteria differed among healthcare areas. For model building, authors preferred value measurement methods and especially the analytical hierarchy process. Authors of the 70 case studies included and confirmed the usefulness of MCDA in different healthcare contexts.The stakeholders, criteria, and MCDA methods chosen differ from one study to another, answering different research objectives in different healthcare areas. This scoping review highlights the importance of each MCDA step (using Belton and Stewart's framework), from problem structuring (including the selection of stakeholders) to the model building phase (encompassing the selection of the appropriate MCDA method). We recommend structured and justified analysis so that decision makers and stakeholders can feel confident during the application process in order to make final decisions more meaningful. Further work is needed to help researchers and decision makers when choosing a suitable MCDA method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Healthcare decision‐making applications using multicriteria decision analysis: A scoping review

Glaize

Duenas²,

Martinelly

et al. 2019

Multi Criteria Decision Anal

View full text Add to dashboard Cite

show abstract

“…This cancer type accounts for 25% of all cancers among women and affects 10% of women globally at some stage of their life [3]. This is a more common issue in developing countries where the mortality rate is greater due to the prohibitive cost incurred for extensive diagnostic tests and treatments required to treat breast cancer completely [4].…”

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data

Rajendran¹,

Jayabalan²,

Thiruchelvam³

2020

IJACSA

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A widespread global health concern among women is the incidence of the second most leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer based on the risk factors will pave the way to an early diagnosis and an efficient treatment in a quicker time. Although there are many predictive models developed for breast cancer in the past, most of these models are generated from highly imbalanced data. The imbalanced data is usually biased towards the majority class but in cancer diagnosis, it is crucial to diagnose the patients with cancer correctly which are oftentimes the minority class. This study attempts to apply three different class balancing techniques namely oversampling (Synthetic Minority Oversampling Technique (SMOTE)), undersampling (SpreadSubsample) and a hybrid method (SMOTE and SpreadSubsample) on the Breast Cancer Surveillance Consortium (BCSC) dataset before constructing the supervised learning methods. The algorithms employed in this study include Naïve Bayes, Bayesian Network, Random Forest and Decision Tree (C4.5). The balancing method which yields the best performance across all the four classifiers were tested using the validation data to determine the final predictive model. The performances of the classifiers were evaluated using a Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity.

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Support to Early Diagnosis of Gestational Diabetes Aided by Bayesian Networks

Filho

Pinheiro

et al. 2019

Advances in Intelligent Systems and Computing

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A Hybrid Model to Support the Early Diagnosis of Breast Cancer

Cited by 24 publications

References 9 publications

Healthcare decision‐making applications using multicriteria decision analysis: A scoping review

Healthcare decision‐making applications using multicriteria decision analysis: A scoping review

Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data

Support to Early Diagnosis of Gestational Diabetes Aided by Bayesian Networks

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