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Decision Support Systems for Disease Detection and Diagnosis

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 33745

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Dr. Maria Rizzi

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Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Interests: computer aided detection and diagnosis systems for biomedical signals; monitoring systems for health-care; analysis and synthesis of digital electronic systems
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Special Issue Information

Dear Colleagues,

The fusion of technology and medical science produces significant innovations that greatly contribute to human health and to people’s quality of life. In fact, health technology helps clinicians to screen abnormalities and contributes to the diagnosis of clinical signs. Projections into the future predict an even greater role for technology in medical practice, especially in the application of computer-aided equipment. In particular, decision support systems (DSS) are becoming important tools in supporting physicians, particularly when both diagnostic signals are complex to analyze (due to their low quality) and a high number of examinations have to be studied.

This Special Issue addresses the most recent research on the development of computer-aided detection and diagnosis systems able to assist medical staff during clinical routine in detecting/diagnosis diseases. In particular, topics of interest for this Special Issue include but are not limited to the following:

New approaches for biosignal/bioimage processing and analysis;
Methods for classification of biosignals;
Biosignal/bioimage feature extraction;
Procedures to improve the quality of biosignals;
Artificial Intelligence tools for biosignal/bioimage analysis.

Dr. Maria Rizzi
Guest Editor

Manuscript Submission Information

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Published Papers (11 papers)

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Research

15 pages, 1209 KiB

Open AccessArticle

A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis

by Grazia Cicirelli and Tiziana D’Orazio

Appl. Sci. 2023, 13(1), 278; https://doi.org/10.3390/app13010278 - 26 Dec 2022

Cited by 2 | Viewed by 3062

Abstract

The observation of mobility tests can greatly help neurodegenerative disease diagnosis. In particular, among the different mobility protocols, the sit-to-stand (StS) test has been recognized as very significant as its execution, both in terms of duration and postural evaluation, can indicate the presence of neurodegenerative diseases and their advancement level. The assessment of an StS test is usually done by physicians or specialized physiotherapists who observe the test and evaluate the execution. Thus, it mainly depends on the experience and expertise of the medical staff. In this paper, we propose an automatic visual system, based on a low-cost camera, that can be used to support medical staff for neurodegenerative disease diagnosis and also to support mobility evaluation processes in telehealthcare contexts. The visual system observes people while performing an StS test, then the recorded videos are processed to extract relevant features based on skeleton joints. Several machine learning approaches were applied and compared in order to distinguish people with neurodegenerative diseases from healthy subjects. Real experiments were carried out in two nursing homes. In light of these experiments, we propose the use of a quadratic SVM, which outperformed the other methods. The obtained results were promising. The designed system reached an accuracy of

95.2 %

demonstrating its effectiveness. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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15 pages, 4723 KiB

Open AccessArticle

Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry Study

by Hsi-Chieh Lee, Tsung-Chieh Lin, Chi-Chang Chang, Yen-Chiao Angel Lu, Chih-Min Lee and Bolormaa Purevdorj

Appl. Sci. 2022, 12(24), 12520; https://doi.org/10.3390/app122412520 - 7 Dec 2022

Cited by 1 | Viewed by 1275

Abstract

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Using data from the 1248 skin cancer survivors extracted from five cancer registries, we benchmarked a random forest algorithm against MLP, C4.5, AdaBoost, and bagging algorithms for several metrics. Additionally, in this study, we leveraged the synthetic minority over-sampling technique (SMOTE) for the issue of the imbalanced dataset, cost-sensitive learning for risk assessment, and SHAP for the analysis of feature importance. The proposed random forest outperformed the other models, with an accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 value of 90.7% in the SPSC category based on 10-fold cross-validation on a balanced dataset. Our results suggest that the four features, i.e., age, stage, gender, and involvement of regional lymph nodes, which significantly affect the output of the prediction model, need to be considered in the analysis of the next causal effect. In addition to causal analysis of specific primary sites, these clinical features allow further investigation of secondary cancers among skin cancer survivors. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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26 pages, 9220 KiB

Open AccessArticle

Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers

by Mehmet Tahir Huyut, Andrei Velichko and Maksim Belyaev

Appl. Sci. 2022, 12(23), 12180; https://doi.org/10.3390/app122312180 - 28 Nov 2022

Cited by 13 | Viewed by 1511

Abstract

Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in August–December, 2021. In this study, the histogram-based gradient-boosting (HGB) model was the most successful machine-learning classifier in detecting living and deceased COVID-19 patients (with squared F1 metrics F1² = 1). The most efficient binary combinations with procalcitonin were obtained with D-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature pairs correctly detected almost all of the patients who survived and those who died (precision > 0.98, recall > 0.98, F1² > 0.98). Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F1² = 0.96) and ferritin (F1² = 0.91). In addition, according to the two-threshold approach, ferritin values between 376.2 μg/L and 396.0 μg/L (F1² = 0.91) and procalcitonin values between 0.2 μg/L and 5.2 μg/L (F1² = 0.95) were found to be fatal risk levels for COVID-19. Considering all the results, we suggest that many features combined with these features, especially procalcitonin and ferritin, operated with the HGB model, can be used to achieve very successful results in the classification of those who live, and those who die from COVID-19. Moreover, we strongly recommend that clinicians consider the critical levels we have found for procalcitonin and ferritin properties, to reduce the lethality of the COVID-19 disease. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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21 pages, 4649 KiB

Open AccessArticle

An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods

by Zari Farhadi, Hossein Bevrani, Mohammad-Reza Feizi-Derakhshi, Wonjoon Kim and Muhammad Fazal Ijaz

Appl. Sci. 2022, 12(20), 10608; https://doi.org/10.3390/app122010608 - 20 Oct 2022

Cited by 4 | Viewed by 1648

Abstract

Nowadays, in the topics related to prediction, in addition to increasing the accuracy of existing algorithms, the reduction of computational time is a challenging issue that has attracted much attention. Since the existing methods may not have enough efficiency and accuracy, we use a combination of machine-learning algorithms and statistical methods to solve this problem. Furthermore, we reduce the computational time in the testing model by automatically reducing the number of trees using penalized methods and ensembling the remaining trees. We call this efficient combinatorial method “ensemble of clustered and penalized random forest (ECAPRAF)”. This method consists of four fundamental parts. In the first part, k-means clustering is used to identify homogeneous subsets of data and assign them to similar groups. In the second part, a tree-based algorithm is used within each cluster as a predictor model; in this work, random forest is selected. In the next part, penalized methods are used to reduce the number of random-forest trees and remove high-variance trees from the proposed model. This increases model accuracy and decreases the computational time in the test phase. In the last part, the remaining trees within each cluster are combined. The results of the simulation and two real datasets based on the WRMSE criterion show that our proposed method has better performance than the traditional random forest by reducing approximately 12.75%, 11.82%, 12.93%, and 11.68% and selecting 99, 106, 113, and 118 trees for the ECAPRAF–EN algorithm. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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15 pages, 2129 KiB

Open AccessArticle

The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations

by Yuhan Du, Anna Markella Antoniadi, Catherine McNestry, Fionnuala M. McAuliffe and Catherine Mooney

Appl. Sci. 2022, 12(20), 10323; https://doi.org/10.3390/app122010323 - 13 Oct 2022

Cited by 8 | Viewed by 3115

Abstract

Explainable artificial intelligence (XAI) has shown benefits in clinical decision support systems (CDSSs); however, it is still unclear to CDSS developers how to select an XAI method to optimize the advice-taking of healthcare practitioners. We performed a user study on healthcare practitioners based on a machine learning-based CDSS for the prediction of gestational diabetes mellitus to explore and compare two XAI methods: explanation by feature contribution and explanation by example. Participants were asked to make estimates for both correctly and incorrectly predicted cases to determine if there were any over-reliance or self-reliance issues. We examined the weight of advice and healthcare practitioners’ preferences. Our results based on statistical tests showed no significant difference between the two XAI methods regarding the advice-taking. The CDSS explained by either method had a substantial impact on the decision-making of healthcare practitioners; however, both methods may lead to over-reliance issues. We identified the inclination towards CDSS use as a key factor in the advice-taking from an explainable CDSS among obstetricians. Additionally, we found that different types of healthcare practitioners had differing preferences for explanations; therefore, we suggest that CDSS developers should select XAI methods according to their target users. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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23 pages, 7446 KiB

Open AccessArticle

Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms

by Roseline Oluwaseun Ogundokun, Rytis Maskeliūnas and Robertas Damaševičius

Appl. Sci. 2022, 12(19), 10156; https://doi.org/10.3390/app121910156 - 10 Oct 2022

Cited by 18 | Viewed by 3682

Abstract

With the advancement in pose estimation techniques, human posture detection recently received considerable attention in many applications, including ergonomics and healthcare. When using neural network models, overfitting and poor performance are prevalent issues. Recently, convolutional neural networks (CNNs) were successfully used for human posture recognition from human images due to their superior multiscale high-level visual representations over hand-engineering low-level characteristics. However, calculating millions of parameters in a deep CNN requires a significant number of annotated examples, which prohibits many deep CNNs such as AlexNet and VGG16 from being used on issues with minimal training data. We propose a new three-phase model for decision support that integrates CNN transfer learning, image data augmentation, and hyperparameter optimization (HPO) to address this problem. The model is used as part of a new decision support framework for the optimization of hyperparameters for AlexNet, VGG16, CNN, and multilayer perceptron (MLP) models for accomplishing optimal classification results. The AlexNet and VGG16 transfer learning algorithms with HPO are used for human posture detection, while CNN and Multilayer Perceptron (MLP) were used as standard classifiers for contrast. The HPO methods are essential for machine learning and deep learning algorithms because they directly influence the behaviors of training algorithms and have a major impact on the performance of machine learning and deep learning models. We used an image data augmentation technique to increase the number of images to be used for model training to reduce model overfitting and improve classification performance using the AlexNet, VGG16, CNN, and MLP models. The optimal combination of hyperparameters was found for the four models using a random-based search strategy. The MPII human posture datasets were used to test the proposed approach. The proposed models achieved an accuracy of 91.2% using AlexNet, 90.2% using VGG16, 87.5% using CNN, and 89.9% using MLP. The study is the first HPO study executed on the MPII human pose dataset. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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12 pages, 629 KiB

Open AccessArticle

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

by Angela Lombardi, Nicola Amoroso, Loredana Bellantuono, Samantha Bove, Maria Colomba Comes, Annarita Fanizzi, Daniele La Forgia, Vito Lorusso, Alfonso Monaco, Sabina Tangaro, Francesco Alfredo Zito, Roberto Bellotti and Raffaella Massafra

Appl. Sci. 2022, 12(14), 7227; https://doi.org/10.3390/app12147227 - 18 Jul 2022

Cited by 5 | Viewed by 1297

Abstract

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (

67 %

) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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15 pages, 21178 KiB

Open AccessArticle

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

by Maria Rizzi and Cataldo Guaragnella

Appl. Sci. 2022, 12(14), 7007; https://doi.org/10.3390/app12147007 - 11 Jul 2022

Cited by 4 | Viewed by 1393

Abstract

Innovative technologies in dermatology allow for the early screening of skin cancer, which results in a reduction in the mortality rate and surgical treatments. The diagnosis of melanoma is complex not only because of the number of different lesions but because of the high similarity amongst skin lesions of different nature; hence, human vision and physician experience still play a major role. The adoption of automatic systems would aid clinical assessment and make the diagnosis reproducible by eliminating inter- and intra-observer variabilities. In our paper, we describe a computer-aided system for the early diagnosis of melanoma in dermoscopic images. A soft pre-processing phase is performed so as to avoid the loss of details both in texture, colors, and contours, and color-based image segmentation is later carried out using k-means. Features linked to both geometric properties and color characteristics are used to analyze skin lesions through a support vector machine classifier. The PH² public database is used for the assessment of the procedure’s sensitivity, specificity, and accuracy. A statistical approach is carried out to establish the impact of image quality on performance. The obtained results show remarkable achievements, so our computer-aided approach should be suitable as a Decision Support System for melanoma detection. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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24 pages, 5339 KiB

Open AccessArticle

Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models

by Ali Tariq Nagi, Mazhar Javed Awan, Mazin Abed Mohammed, Amena Mahmoud, Arnab Majumdar and Orawit Thinnukool

Appl. Sci. 2022, 12(13), 6364; https://doi.org/10.3390/app12136364 - 22 Jun 2022

Cited by 22 | Viewed by 2074

Abstract

The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world’s accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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27 pages, 3910 KiB

Open AccessArticle

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

by Sardar Mehboob Hussain, Domenico Buongiorno, Nicola Altini, Francesco Berloco, Berardino Prencipe, Marco Moschetta, Vitoantonio Bevilacqua and Antonio Brunetti

Appl. Sci. 2022, 12(12), 6230; https://doi.org/10.3390/app12126230 - 19 Jun 2022

Cited by 16 | Viewed by 3437

Abstract

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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13 pages, 1622 KiB

Open AccessArticle

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

by Gulay Macin, Burak Tasci, Irem Tasci, Oliver Faust, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Ru-San Tan and U. Rajendra Acharya

Appl. Sci. 2022, 12(10), 4920; https://doi.org/10.3390/app12104920 - 12 May 2022

Cited by 23 | Viewed by 9833

Abstract

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients. Full article

(This article belongs to the Special Issue Decision Support Systems for Disease Detection and Diagnosis)

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