Imbalanced Medical Data Research Articles

A Comprehensive Analysis of a Framework for Rebalancing Imbalanced Medical Data Using an Ensemble-based Classifier

In medical data, addressing imbalanced datasets is paramount for accurate predictive modeling. This paper delves into exploring a well-established rebalancing framework proposed in previous research. While acknowledged for its effectiveness, the adaptability of this framework across diverse medical datasets remains unexplored. We conduct a comprehensive investigation to bridge this gap by integrating an ensemble-based classifier into the existing framework. By leveraging seven imbalanced medical binary datasets, our study comprises three distinct experiments: utilizing standard baseline classifiers from the framework (original), incorporating the baseline with an ensemble-based classifier, and introducing our novel ensemble-based classifier with the self-paced ensemble (SPE) algorithm. Our novel ensemble, composed of decision tree (DT), radial support vector machine (R.SVM), and extreme gradient boosting (XGB) classifiers, serves as the foundation for the SPE. Our primary objective is to demonstrate the potential improvement of the existing framework’s overall performance through the integration of an ensemble. Experimental results reveal significant enhancements, with our proposed ensemble classifier outperforming the original by 4.96%, 5.89%, 5.68%, 7.85%, and 6.84% in terms of accuracy, precision, recall, F-score, and G-mean, respectively. This study contributes valuable insights into the adaptability and performance augmentation achievable through ensemble methods in addressing class imbalances within the medical domain.

Effect of Random Under sampling, Oversampling, and SMOTE on the Performance of Cardiovascular Disease Prediction Models

Cardiovascular Disease (CVD) or commonly known as Heart Disease is a leading cause of mortality globally, prompting extensive research into predictive models to assess individual risk and plan preventive measures. Machine learning approaches such as Random Forest, Support Vector Machine (SVM), and LASSO Logistic Regression have showed promise. Recent studies have indicated that traditional resampling methods like Random Oversampling, Random Undersampling, and SMOTE may not significantly improve model discrimination. This study aims to evaluate the impact of these techniques on the performance of Cardiovascular Disease (CVD) prediction models, utilizing data from the UCI Machine Learning Heart Disease database. By employing LASSO Logistic Regression, Random Forest, and Support Vector Machine (SVM) with resampling techniques, including Random Oversampling, Random Undersampling, and SMOTE. This research seeks to enhance understanding of model performance in addressing class imbalances within the dataset and contribute to refining cardiovascular disease (CVD) prediction strategies. This study demonstrates that the use of the SMOTE technique significantly enhances the performance of cardiovascular disease (CVD) prediction models. Specifically, when combined with the Random Forest algorithm, SMOTE achieves the best performance in terms of accuracy, sensitivity, and specificity. This highlights the importance of selecting appropriate resampling techniques to handle class imbalance in datasets. Consequently, this research contributes to refining CVD prediction strategies and provides new insights into improving prediction accuracy in imbalanced medical data.

Processing imbalanced medical data at the data level with assisted-reproduction data as an example

ObjectiveData imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios.MethodsWe collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness.ResultsThe logistic model’s performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes.ConclusionsThe study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy.

A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data

A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data

Cost-sensitive learning for imbalanced medical data: a review

Integrating Machine Learning (ML) in medicine has unlocked many opportunities to harness complex medical data, enhancing patient outcomes and advancing the field. However, the inherent imbalanced distribution of medical data poses a significant challenge, resulting in biased ML models that perform poorly on minority classes. Mitigating the impact of class imbalance has prompted researchers to explore various strategies, wherein Cost-Sensitive Learning (CSL) arises as a promising approach to improve the accuracy and reliability of ML models. This paper presents the first review of CSL for imbalanced medical data. A comprehensive exploration of the existing literature encompassed papers published from January 2010 to December 2022 and sourced from five major digital libraries. A total of 173 papers were selected, analysed, and classified based on key criteria, including publication years, channels and sources, research types, empirical types, medical sub-fields, medical tasks, CSL approaches, strengths and weaknesses of CSL, frequently used datasets and data types, evaluation metrics, and development tools. The results indicate a noteworthy publication rise, particularly since 2020, and a strong preference for CSL direct approaches. Data type analysis unveiled diverse modalities, with medical images prevailing. The underutilisation of cost-related metrics and the prevalence of Python as the primary programming tool are highlighted. The strengths and weaknesses analysis covered three aspects: CSL strategy, CSL approaches, and relevant works. This study serves as a valuable resource for researchers seeking to explore the current state of research, identify strengths and gaps in the existing literature and advance CSL’s application for imbalanced medical data.

Improving Classification Performance on Imbalanced Medical Data using Generative Adversarial Network

In many real-world applications, the problem of data imbalance is a common challenge that significantly affects the performance of machine learning algorithms. Data imbalance means each target of classes is not balanced. This problem often appears in medical data, where the positive cases of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals.

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

The Performance Comparison between C4.5 Tree and One-Dimensional Convolutional Neural Networks (CNN1D) with Tuning Hyperparameters for the Classification of Imbalanced Medical Data

The Performance Comparison between C4.5 Tree and One-Dimensional Convolutional Neural Networks (CNN1D) with Tuning Hyperparameters for the Classification of Imbalanced Medical Data

CHARACTERIZATION OF MORTALITY PREDICTION: AN ENSEMBLE LEARNING ANALYSIS USING THE MIMIC-III DATASET

Applications that employ medical data are directly impacted by the classification of imbalanced data. It is vital due to the nature of classification and solutions about medical data. The purpose of this article is to identify a machine learning model that may be successfully applied in the medical field to reduce the number of mortality and optimize the efficiency of hospital resources. For this reason, it is thought that the better the performance of the ML model, the more a different perspective will be gained on the problems in today's medicine. Therefore, in the study, Weighted Random Forest (WRF) and Balanced Random Forest (BRF) which are ensemble machine learning (ML) methods for imbalanced data were implemented to identify the performance of the algorithms for mortality determination from open-source MIMIC-III dataset by using vital signs, comorbidities, and laboratory variables with demographic characteristic information. To evaluate the performance of WRF and BRF, a Random Forest Classifier (RFC) was also implemented to investigate the power of developed models for imbalanced data. In addition, the features used in the ML methods were separated into three groups to explore the impact of the vital signs, comorbidities, and laboratory variables with demographic characteristics separately on mortality identification. In addition to previous applications on UCI datasets, the present study revealed that the BRF method for imbalanced medical data provides high performance in determining the majority and minority classes of the data by using vital signs and laboratory variables with demographic characteristics.

Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.

An Oversampling Algorithm combining SMOTE and RF for Imbalanced Medical Data

Abstract: A dataset is said to be imbalanced when the distribution of classes is unbalanced. In medical datasets, this is a typical issue because the positive class, such as the existence of an illness, is frequently rarer than the negative class, such as the absence of sickness. Since machine learning algorithms are prone to being biased in favor of the majority class, this might negatively affect their performance. An oversampling algorithm that combines the SMOTE and KNN SMOTE methods has been presented as a solution to this issue. Synthetic minority oversampling, a well-known oversampling approach, interpolates between existing samples to produce synthetic samples for the minority class. The KNN, on the other hand, chooses K's nearest neighbors, mixes them, and produces synthetic samples in space. The oversampling algorithm can be used to balance the dataset, and then the random forest decision tree approach can be used to create a classification model. The random forest algorithm chooses random samples from a set of data and builds a decision tree for each training set of data. Voting is used to determine the decision tree's average, and the prediction result with the highest number of votes is chosen as the final prediction outcome.

Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data.

Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and generated three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition. We train a system to detect AD-related signs and symptoms from EHRs. We find that the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.

Flexible cloglog links for binomial regression models as an alternative for imbalanced medical data.

The complementary log-log link was originally introduced in 1922 to R. A. Fisher, long before the logit and probit links. While the last two links are symmetric, the complementary log-log link is an asymmetrical link without a parameter associated with it. Several asymmetrical links with an extra parameter were proposed in the literature over last few years to deal with imbalanced data in binomial regression (when one of the classes is much smaller than the other); however, these do not necessarily have the cloglog link as a special case, with the exception of the link based on the generalized extreme value distribution. In this paper, we introduce flexible cloglog links for modeling binomial regression models that include an extra parameter associated with the link that explains some unbalancing for binomial outcomes. For all cases, the cloglog is a special case or the reciprocal version loglog link is obtained. A Bayesian Markov chain Monte Carlo inference approach is developed. Simulations study to evaluate the performance of the proposed algorithm is conducted and prior sensitivity analysis for the extra parameter shows that a uniform prior is the most convenient for all models. Additionally, two applications in medical data (age at menarche and pulmonary infection) illustrate the advantages of the proposedmodels.

BiLSTM deep neural network model for imbalanced medical data of IoT systems

Health informatics is one of the most developed field in recent time. Computational Intelligence is among the most influential factors that may help to improve patient oriented and secure decision support model. In this article we present a model of IoT system, which combines BiLSTM deep learning with Decision Tree model and data balancing strategy used to help in automated diagnosis support. Presented solution include experimental series of data preprocessing using well established balancing algorithms with custom parameters and modifications in order to best prepare the data for the network training. Such algorithms are ADASYN, SMOTE-Tomek, etc. The system helps to evaluate questionnaires and securely exchange documents between patient and corresponding medical team. From the level of system patient and doctors are able to see automated diagnosis provided by deep learning model. The model gives an important advance to help patients faster. Results show that proposed BiLSTM deep learning with decision tree mode detects diseases from questionnaires with accuracy above 96%, precision above 88% and recall above 96% which proves efficiency of our proposed model.

Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data.

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.e., retrogress) after each communication and the intractable class imbalance. Thus, in this paper, we propose a novel personalized FL framework to tackle these two problems. For the retrogress problem, we first devise a Progressive Fourier Aggregation (PFA) at the server side to gradually integrate parameters of client models in the frequency domain. Then, at the client side, we design a Deputy-Enhanced Transfer (DET) to smoothly transfer global knowledge to the personalized local model. For the class imbalance problem, we propose the Conjoint Prototype-Aligned (CPA) loss to facilitate the balanced optimization of the FL framework. Considering the inaccessibility of private local data to other participants in FL, the CPA loss calculates the global conjoint objective based on global imbalance, and then adjusts the client-side local training through the prototype-aligned refinement to eliminate the imbalance gap with such a balanced goal. Extensive experiments are performed on real-world dermoscopic and prostate MRI FL datasets. The experimental results demonstrate the advantages of our FL framework in real-world medical scenarios, by outperforming state-of-the-art FL methods with a large margin. The source code is available at https://github.com/CityU-AIM-Group/PRR-Imbalancehttps://github.com/CityU-AIM-Group/PRR-Imbalance.

Multi-domain medical image translation generation for lung image classification based on generative adversarial networks

ObjectiveLung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks. MethodsThis paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients. ResultsBased on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%. ConclusionIn this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally.

LDADN: a local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection.

Pneumoconiosis is deemed one of China's most common and serious occupational diseases. Its high prevalence and treatment cost create enormous pressure on socio-economic development. However, due to the scarcity of labeled data and class-imbalanced training sets, the computer-aided diagnostic based on chest X-ray (CXR) images of pneumoconiosis remains a challenging task. Current CXR data augmentation solutions cannot sufficiently extract small-scaled features in lesion areas and synthesize high-quality images. Thus, it may cause error detection in the diagnosis phase. In this paper, we propose a local discriminant auxiliary disentangled network (LDADN) to synthesize CXR images and augment in pneumoconiosis detection. This model enables the high-frequency transfer of details by leveraging batches of mutually independent local discriminators. Cooperating with local adversarial learning and the Laplacian filter, the feature in the lesion area can be disentangled by a single network. The results show that LDADN is superior to other compared models in the quantitative assessment metrics. When used for data augmentation, the model synthesized image significantly boosts the performance of the detection accuracy to 99.31%. Furthermore, this study offers beneficial references for insufficient label or class imbalanced medical image data analysis.

Classification of imbalanced medical data: An empirical study of machine learning approaches

Thousands of patients around the world affecting their health with various factor as age, body mass index, cholesterol levels, albumin levels and several other factor. Prediction of health outcome due to these factors at a proper time can be served as an early warning. Recent growth in machine learning algorithm inspired us to build a predictive model for better healthcare facilities. In our work we have focused on problem of noisy and imbalanced dataset in which majority class is favored over minority one that leads to false prediction. We have experimented with two publicly available medical imbalanced dataset which varies in its size as MIT’s GOSSIS death and PIMA Indians Diabetes Dataset based on binary class. In this model we have investigated 3 oversampling techniques (Synthetic Minority Oversampler, Random Oversampler and Adaptive Synthetic Sampler) along with two undersampling techniques (Random Undersampler and Near Miss) which were paired with 3 data reduction and cleaning methods namely Tomek Links, One Sided Selection and Edited Nearest Neighbors. At last, we found that combination of Adaptive Synthetic Sampler along with One Sided Selection perform better in case of large size dataset while combination of random oversampler along with Tomek Link showed better performance in case of low size data dataset. We have also analyzed that oversampling technique gives quite promising results in comparison to undersampling methods specifically when applied with machine learning classifiers as these classifiers are data hungry algorithms.

Tversky Similarity based UnderSampling with Gaussian Kernelized Decision Stump Adaboost Algorithm for Imbalanced Medical Data Classification

In recent years, imbalanced data classification are utilized in several domains including, detecting fraudulent activities in banking sector, disease prediction in healthcare sector and so on. To solve the Imbalanced classification problem at data level, strategy such as undersampling or oversampling are widely used. Sampling technique pose a challenge of significant information loss. The proposed method involves two processes namely, undersampling and classification. First, undersampling is performed by means of Tversky Similarity Indexive Regression model. Here, regression along with the Tversky similarity index is used in analyzing the relationship between two instances from the dataset. Next, Gaussian Kernelized Decision stump AdaBoosting is used for classifying the instances into two classes. Here, the root node in the Decision Stump takes a decision on the basis of the Gaussian Kernel function, considering average of neighboring points accordingly the results is obtained at the leaf node. Weights are also adjusted to minimizing the training errors occurring during classification to find the best classifier. Experimental assessment is performed with two different imbalanced dataset (Pima Indian diabetes and Hepatitis dataset). Various performance metrics such as precision, recall, AUC under ROC score and F1-score are compared with the existing undersampling methods. Experimental results showed that prediction accuracy of minority class has improved and therefore minimizing false positive and false negative.

A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data

The algorithm of C4.5 decision tree has the advantages of high classification accuracy , fast calculation speed and comprehensible classification rules, so it is widely used for medical data analysis. However, for imbalanced medical data, the classification accuracy of decision trees-based models is not ideal. Therefore, this paper proposes a cluster-based oversampling algorithm (KNSMOTE) combining Synthetic minority oversampling technique (SMOTE) and k -means algorithm. The sample classes clustered by k -means and the original sample classes are calculated to select the ‘‘safe samples” whose sample classes have not been changed. The ‘‘safe samples” are linearly interpolated to synthesize the new samples. The improved SMOTE sets the oversampling ratio according to the imbalance ratio of the original samples, which is used to synthesize the samples whose number is the same as that of the original samples. Compared with other oversampling algorithms on 8 UCI datasets, our algorithm has achieved significant advantages. Our algorithm was applied to the medical datasets, and the average values of the Sensitivity and Specificity indexes of the Random forest (RF) algorithm were 99.84% and 99.56%, respectively.