A wrapper-based feature selection approach to investigate potential biomarkers for early detection of breast cancer

Alnowami, Majdi R.; Abolaban, Fouad A.; Taha, Eslam

doi:10.1016/j.jrras.2022.01.003

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…The result showed that the proposed model successfully classified breast cancer by employing six features, such as BMI, age, levels of glucose, MCP-1, insulin, and resistin. Alnowami et al [18] utilized three ML algorithms, i.e., DT, RF, and DT, and combined them with a sequential backward-selection model. The result showed that the optimal set of biomarkers such as levels of glucose, BMI, resistin, age, and HOMA can be utilized as features for the SVM model.…”

Section: Related Workmentioning

confidence: 99%

“…Support vector machine (SVM) is an ML model that divides instances of each class from the others by locating the linear optimum hyperplane after nonlinearly mapping the original data into a high-dimensional feature space. SVMs have demonstrated superior performance for breast cancer detection as compared to conventional models [15][16][17][18]. Additionally, earlier research has demonstrated the beneficial effects of using extra-tree as the feature selection approach to increase classification accuracy in natural language processing [19], white blood cell classification [20], and network intrusion detection [21].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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“…The characteristics were compared between Plasmodium falciparum positive and negative using the Wilcoxon Rank Sum test for continuous variables and Pearson's Chisquare test for categorical variables, with Yates's continuity correction when appropriate. (Nadeem & Jabri, 2023,Dumitrescu et al, 2022, Bayesian generalised model (Modabbernia et al, 2022;Kamau et al, 2022) decision tree (Li et al, 2022;Avanceña et al, 2022;Dasgupta et al, 2022)modelswithnested crossvalidation (Parvandeh et al, 2020;Tu et al, 2022)for parameters optimisation and a wrapperbased sequential backward features selection (Alnowami et al, 2022) Nested cross-validation. Nested cross-validations involving multiple layers of crossvalidation: inner and outer folds, were carried out on the training data set to obtain reliable classification accuracy and avoid over fitting (Parvandeh et al, 2020;Tu et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Ojurongbe

Afolabi

Akinde

et al. 2023

Preprint

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Background: The current malaria diagnosis methods, which rely on microscopy and Histidine Rich Protein2 (HRP2) based RDT, have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all the limitations. Consequently, automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. This study, therefore, used a machine learning model to predict the occurrence of malaria based on socio-demographicbehaviour, environment, and clinical features. Methods:Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward features selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Results: Among the three machine learningmodels examined, penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve performance for the training set (84%; 95% confidence interval (CI) =75–93%) and test set (83%; 95% CI =63–100%). The model included age, BMI, body temperature, bushes in surroundings, body weight, dizziness, fever, headache, mosquito repellant, muscle pain, sex, sore throat, stagnant water in the home, and vomiting. An increased odd of patients having malaria was associated with high body weight (adjusted odd ratio (AOR) = 4.50, 95% CI =2.27-8.01, p-value <0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients who had body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI =0.99-1.91, p-value = 0.068). Also, patients who had bushes in the surroundings (AOR = 2.60, 95% CI =1.30-4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI =0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI =0.95–3.95, p-value = 0.068), muscle pain (AOR =1.49; CI =0.66–3.39, p-value = 0.333) and vomiting (AOR = 2.32; CI =0.85–6.82, p-value = 0.097) were more likely to experience malaria compared to those without bushes in the surrounding or those who did not experience fever, headache, muscle pain and vomiting. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI= 0.41-0.90, p-value = 0.012) or BMI (AOR = 0.47, 95% CI= 0.26-0.80, p-value =0.006). Conclusion:Newly developed routinely collected baseline socio-demographical, environmental, and clinical features topredict malaria types and may serve as a valuable tool for clinical decision making.

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“…2.4.2 Models Development. Penalised logistic regression ( 41), ( 42), Bayesian generalised model ( 43), (44), and decision tree model ( 45), ( 46), (47) with nested cross-validation (48), (49) for parameter optimisation and wrapper-based sequential backward feature selection (50) were employed to determine the malaria type (Plasmodium falciparum positive or negative). Eighty percent of the samples (one hundred sixty samples consisting of twenty-eight and one hundred thirty-two Plasmodium falciparum positive and negative samples, respectively) were used for model training.…”

Section: Methodsmentioning

confidence: 99%

“…2.4.5 Optimal Feature Selection and Hyperparameters. Feature selection was performed using sequential backward search selection (SBSS) for each inner training set (50). The SBSS started with all features and dropped the non-informative features at each iteration, improving the model's performance.…”

Section: Datamentioning

confidence: 99%

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Ojurongbe

Afolabi

Bashiru

et al. 2023

Preprint

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Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all of these limitations. Consequently, the automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. Therefore, this study used a machine-learning model to predict the occurrence of malaria based on sociodemographic behaviour, environment, and clinical features.Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models.Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve for the training set (84%; 95% confidence interval (CI) = 75–93%) and test set (83%; 95% CI = 63–100%). Increased odds of malaria was associated with high body weight (adjusted odds ratio (AOR) = 4.50, 95% CI = 2.27–8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients with body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI = 0.99–1.91, p-value = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI = 1.30–4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI = 0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI = 0.95–3.95, p-value = 0.068), muscle pain (AOR = 1.49; CI = 0.66–3.39, p-value = 0.333), and vomiting (AOR = 2.32; CI = 0.85–6.82, p-value = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI = 0.41–0.90, p-value = 0.012) and BMI (AOR = 0.47, 95% CI = 0.26–0.80, p = 0.006).Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict malaria types may serve as a valuable tool for clinical decision-making.

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A wrapper-based feature selection approach to investigate potential biomarkers for early detection of breast cancer

Cited by 14 publications

References 27 publications

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

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