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Potential Value and Impact of Machine Learning in Clinical Classification and Prediction

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 23017

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Special Issue Editor

Dr. Chunhung Richard Lin

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Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
Interests: network; routing; computer networking; network architecture; network communication; QoS; networking; cloud computing; TCP; wireless computing
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Dear Colleagues,

Recently, artificial intelligence (AI) has been widely used in medicine and healthcare. In machine learning, classification/prediction is one of the main areas of AI. Currently, there is active research on existing prediction models based on machine-learning methods. Machine-learning algorithms have improved the ability to predict the risk of complex diseases. This improved predictive capability stems from the ability of machine-learning algorithms to process multidimensional data. A great deal of research has been conducted on predicting the future state of patients. Future applications of machine learning predictive models may help manage complex diseases by providing tissue-specific targets for customized, preventive interventions.

Machine learning can also be used to aid in diagnosis and prognosis. Recent efforts to use machine learning for diagnosis have shifted from classification of a given disease to differential diagnosis. In particular, data mining techniques are now increasingly used in clinical diagnosis, and they have the potential to support this area.

We are pleased to invite you to contribute to this Special Issue on the potential value and impact of machine learning in clinical classification and prediction with original contributions and review articles focusing on recent and related advances in the field, including the latest trends and future developments.

Dr. Chunhung Richard Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (6 papers)

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Research

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19 pages, 19471 KiB

Open AccessArticle

Machine Learning Models for Predicting Adverse Pregnancy Outcomes in Pregnant Women with Systemic Lupus Erythematosus

by Xinyu Hao, Dongying Zheng, Muhanmmad Khan, Lixia Wang, Timo Hämäläinen, Fengyu Cong, Hongming Xu and Kedong Song

Diagnostics 2023, 13(4), 612; https://doi.org/10.3390/diagnostics13040612 - 7 Feb 2023

Cited by 5 | Viewed by 2577

Abstract

Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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18 pages, 3774 KiB

Open AccessArticle

Deep Learning Technology Applied to Medical Image Tissue Classification

by Min-Jen Tsai and Yu-Han Tao

Diagnostics 2022, 12(10), 2430; https://doi.org/10.3390/diagnostics12102430 - 7 Oct 2022

Cited by 3 | Viewed by 2286

Abstract

Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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19 pages, 5050 KiB

Open AccessArticle

Melanoma Detection Using XGB Classifier Combined with Feature Extraction and K-Means SMOTE Techniques

by Chih-Chi Chang, Yu-Zhen Li, Hui-Ching Wu and Ming-Hseng Tseng

Diagnostics 2022, 12(7), 1747; https://doi.org/10.3390/diagnostics12071747 - 19 Jul 2022

Cited by 24 | Viewed by 2844

Abstract

Melanoma, a very severe form of skin cancer, spreads quickly and has a high mortality rate if not treated early. Recently, machine learning, deep learning, and other related technologies have been successfully applied to computer-aided diagnostic tasks of skin lesions. However, some issues in terms of image feature extraction and imbalanced data need to be addressed. Based on a method for manually annotating image features by dermatologists, we developed a melanoma detection model with four improvement strategies, including applying the transfer learning technique to automatically extract image features, adding gender and age metadata, using an oversampling technique for imbalanced data, and comparing machine learning algorithms. According to the experimental results, the improved strategies proposed in this study have statistically significant performance improvement effects. In particular, our proposed ensemble model can outperform previous related models. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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25 pages, 44185 KiB

Open AccessArticle

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

by Happy Nkanta Monday, Jianping Li, Grace Ugochi Nneji, Saifun Nahar, Md Altab Hossin, Jehoiada Jackson and Chukwuebuka Joseph Ejiyi

Diagnostics 2022, 12(3), 741; https://doi.org/10.3390/diagnostics12030741 - 18 Mar 2022

Cited by 12 | Viewed by 6175

Abstract

Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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Review

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17 pages, 1771 KiB

Open AccessReview

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

by Syrine Ghrabli, Mohamed Elgendi and Carlo Menon

Diagnostics 2022, 12(9), 2142; https://doi.org/10.3390/diagnostics12092142 - 2 Sep 2022

Cited by 9 | Viewed by 3064

Abstract

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual’s health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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24 pages, 2674 KiB

Open AccessReview

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

by Arti Rana, Ankur Dumka, Rajesh Singh, Manoj Kumar Panda, Neeraj Priyadarshi and Bhekisipho Twala

Diagnostics 2022, 12(8), 2003; https://doi.org/10.3390/diagnostics12082003 - 19 Aug 2022

Cited by 49 | Viewed by 5251

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation. Full article

(This article belongs to the Special Issue Potential Value and Impact of Machine Learning in Clinical Classification and Prediction)

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