Meta-Health Stack: A new approach for breast cancer prediction

Samieinasab, Mina; Torabzadeh, Seyed Ahmad; Behnam, Arman; Aghsami, Amir; Jolai, Fariborz

doi:10.1016/j.health.2021.100010

Cited by 24 publications

(12 citation statements)

References 26 publications

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“…RF and gradient boosting combine the results of a DT for better prediction. Also, gradient boosting is ensemble tree-based methods applying the principle of gradient descent [ 47 ]. The RF divides the data into some random subset and trains them in parallel, ultimately using the majority of votes for the final prediction.…”

Section: Methodsmentioning

confidence: 99%

Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

Mazloumi

Abazari

Nafarieh

et al. 2022

Neural Comput & Applic

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This study’s main purpose is to provide helpful information using blood samples from COVID-19 patients as a non-medical approach for helping healthcare systems during the pandemic. Also, this paper aims to evaluate machine learning algorithms for predicting the survival or death of COVID-19 patients. We use a blood sample dataset of 306 infected patients in Wuhan, China, compiled by Tangji Hospital. The dataset consists of blood’s clinical indicators and information about whether patients are recovering or not. The used methods include K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), bagging classifier (BC), and adaptive boosting (AdaBoost). We compare the performance of machine learning algorithms using statistical hypothesis testing. The results show that the most critical feature is age, and there is a high correlation between LD and CRP, and leukocytes and CRP. Furthermore, RF, SVM, DT, AdaBoost, DT, and KNN outperform other machine learning algorithms in predicting the survival or death of COVID-19 patients.

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Section: Methodsmentioning

confidence: 99%

Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

Mazloumi

Abazari

Nafarieh

et al. 2022

Neural Comput & Applic

Self Cite

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“…Just like gene expression datasets, different strategies of feature reduction have been utilized to enhance breast cancer classification accuracy based on WDBC dataset. Multiple feature selection methods have been used to build Meta Health Stack by Samieinasab et al [ 47 ]. In this model, the Extra Trees classifier is used to combine features resulting from the Variance Inflation Factor, Pearson’s Correlation and Information Gain.…”

Section: Related Workmentioning

confidence: 99%

“…[ 26 ] 2022 4 - SMOTE Resampling + L2-SVM Kakati et al [ 27 ] 2022 17 - Transfer learning + CNN Dai et al [ 28 ] 2021 3 - ERGCN Single Stage Feature Selection Mohammed et al [ 29 ] 2021 5 Lasso Staking Ensemble of CNN Menaga et al. [ 30 ] 2021 2 Wrapper Fractional-ASO Deep RNN Al Mamun et al [ 31 ] 2021 12 mrCAE - Multiple Stages Feature Selection Majumder et al [ 33 ] 2022 4 ANOVA, IG MLP, 1DCNN, 2DCNN Saberi-Movahed et al [ 34 ] 2022 9 DR-FS-MFMR = Matrix Factorization + Minimum Redundancy Unsupervised clustering Bustamam et al [ 35 ] 2021 2 SVM-RFE + ABC SVM Samieinasab et al [ 47 ] 2022 1 Ensemble (Variance Inflation Factor, Pearson’s Correlation, Information G...…”

Section: Related Workmentioning

confidence: 99%

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

et al. 2023

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Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets. These characteristics have a negative impact on the performance of most classifiers when used to classify cancer using genomic datasets. Results This paper introduces Reduced Noise-Autoencoder (RN-Autoencoder) for pre-processing imbalanced genomic datasets for precise cancer classification. Firstly, RN-Autoencoder solves the curse of dimensionality problem by utilizing the autoencoder for feature reduction and hence generating new extracted data with lower dimensionality. In the next stage, RN-Autoencoder introduces the extracted data to the well-known Reduced Noise-Synthesis Minority Over Sampling Technique (RN- SMOTE) that efficiently solve the problem of class imbalance in the extracted data. RN-Autoencoder has been evaluated using different classifiers and various imbalanced datasets with different imbalance ratios. The results proved that the performance of the classifiers has been improved with RN-Autoencoder and outperformed the performance with original data and extracted data with percentages based on the classifier, dataset and evaluation metric. Also, the performance of RN-Autoencoder has been compared to the performance of the current state of the art and resulted in an increase up to 18.017, 19.183, 18.58 and 8.87% in terms of test accuracy using colon, leukemia, Diffuse Large B-Cell Lymphoma (DLBCL) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. Conclusion RN-Autoencoder is a model for cancer classification using imbalanced gene expression datasets. It utilizes the autoencoder to reduce the high dimensionality of the gene expression datasets and then handles the class imbalance using RN-SMOTE. RN-Autoencoder has been evaluated using many different classifiers and many different imbalanced datasets. The performance of many classifiers has improved and some have succeeded in classifying cancer with 100% performance in terms of all used metrics. In addition, RN-Autoencoder outperformed many recent works using the same datasets.

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“…The meta learner learns the combination weights for all base level decision probabilities, and classifies instances. For a stack ensemble to perform well, it is important to promote information gain of features used for training the meta learner through level 0 base learners [19,20]. The proposed framework is motivated by this rationale.…”

Section: B Proposed Framework Ss-ilmentioning

confidence: 99%

A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction

Prabu

2022

IEEE Access

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Churn prediction is gaining popularity in the research community as a powerful paradigm that supports data-driven operational decisions. Datasets related to churn prediction are often skewed with imbalanced class distribution. Data-level solutions, like over-sampling and under-sampling, have been commonly used by researchers to address this problem. There are limited number of case studies that attempt to evolve these data-level solutions by integrating them with computationally advanced frameworks, like ensembles. Ensembles primarily employ algorithmic diversity using a fixed set of training instances to achieve superior performance. This study aims to introduce algorithmic diversity in ensembles by modifying the fixed set of training instances using diverse sampling strategies to increase predictive performance in imbalanced learning. Data is acquired from the world's largest open hotel commerce platform company. A four-part series of experiments is conducted to analyze the effectiveness of sampling techniques and ensemble solutions on model performance. A new sampling-based stack framework called "Stacking of Samplers for Imbalanced Learning" is proposed. The framework combines the prediction capabilities of sampling solutions to stimulate the information gain of the meta features in ensemble. It is observed that the proposed framework leads to improvement in model performance with AUC of 86.4% and top-decile lift of 4.7 for customers of the hotel technology provider. Additionally, results show that the framework records a higher information gain for meta features used in a stack, compared to commonly used stack frameworks.

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Meta-Health Stack: A new approach for breast cancer prediction

Cited by 24 publications

References 26 publications

Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction

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