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Artificial Intelligence Technology 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 (30 June 2023) | Viewed by 21748

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Prof. Dr. Cemil Colak

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Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya, Turkey
Interests: medicine; fundamental medical sciences; biostatistics and medical informatics; epidemiology; information security and reliability; computer vision; bioinformatics; artificial intelligence; computer learning and pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sami Akbulut

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Department of Surgery, Public Health and Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Turkey
Interests: liver transplantation; hepatobiliary surgery; hydatid disease; appendiceal disease; public health; epidemiological studies; biostatistical studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ever-increasing amount of available medical data, the use of artificial intelligence (AI) technology to help diagnose diseases, provide medical solutions, and improve the quality of treatment and care has become widespread. AI technology solves medical problems by analyzing large amounts of data and extracting useful, high-level information from these data with little or no human supervision. AI helps modernize procedures to ease clinicians' workload, automate medicine activities, reduce healthcare costs, quickly share data, and streamline operations, leading to improved prevention strategies, efficiency, and patient retention times, as well as faster and more accurate results.

This Special Issue welcomes original research and review articles applying AI technologies and algorithms to automate traditional healthcare systems and computer-assisted diagnosis.

Potential topics include, but are not limited to:

Computer-assisted detection and diagnosis;
Interpretable artificial intelligence for an improved understanding of biomedical data;
AI for precision medicine and preventive healthcare;
AI for disease diagnosis and treatment in bioinformatics;
AI technologies for multiomics data;
AI- and machine-learning-based diagnosis and treatment applications;
Big data analytics on biomedical applications;
Computerized clinical practice / clinical guidelines and protocols.

Prof. Dr. Cemil Colak
Prof. Dr. Sami Akbulut
Guest Editors

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.

Keywords

tumor classification
cancer diagnosis
bioinformatics
metabolomics
anomaly detection
big data processing
classification
data mining
machine learning
deep learning
knowledge discovery process
computer-assisted diagnosis systems

Published Papers (11 papers)

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Research

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13 pages, 1598 KiB

Open AccessArticle

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification

by Ghada Atteia, Rana Alnashwan and Malak Hassan

Diagnostics 2023, 13(16), 2672; https://doi.org/10.3390/diagnostics13162672 - 14 Aug 2023

Viewed by 1216

Abstract

Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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14 pages, 1107 KiB

Open AccessArticle

A First Computational Frame for Recognizing Heparin-Binding Protein

by Wen Zhu, Shi-Shi Yuan, Jian Li, Cheng-Bing Huang, Hao Lin and Bo Liao

Diagnostics 2023, 13(14), 2465; https://doi.org/10.3390/diagnostics13142465 - 24 Jul 2023

Cited by 16 | Viewed by 911

Abstract

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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18 pages, 1954 KiB

Open AccessArticle

Prediction of Diabetic Macular Edema Using Knowledge Graph

by Zhi-Qing Li, Zi-Xuan Fu, Wen-Jun Li, Hao Fan, Shu-Nan Li, Xi-Mo Wang and Peng Zhou

Diagnostics 2023, 13(11), 1858; https://doi.org/10.3390/diagnostics13111858 - 26 May 2023

Cited by 1 | Viewed by 1454

Abstract

Diabetic macular edema (DME) is a significant complication of diabetes that impacts the eye and is a primary contributor to vision loss in individuals with diabetes. Early control of the related risk factors is crucial to reduce the incidence of DME. Artificial intelligence (AI) clinical decision-making tools can construct disease prediction models to aid in the clinical screening of the high-risk population for early disease intervention. However, conventional machine learning and data mining techniques have limitations in predicting diseases when dealing with missing feature values. To solve this problem, a knowledge graph displays the connection relationships of multi-source and multi-domain data in the form of a semantic network to enable cross-domain modeling and queries. This approach can facilitate the personalized prediction of diseases using any number of known feature data. In this study, we proposed an improved correlation enhancement algorithm based on knowledge graph reasoning to comprehensively evaluate the factors that influence DME to achieve disease prediction. We constructed a knowledge graph based on Neo4j by preprocessing the collected clinical data and analyzing the statistical rules. Based on reasoning using the statistical rules of the knowledge graph, we used the correlation enhancement coefficient and generalized closeness degree method to enhance the model. Meanwhile, we analyzed and verified these models’ results using link prediction evaluation indicators. The disease prediction model proposed in this study achieved a precision rate of 86.21%, which is more accurate and efficient in predicting DME. Furthermore, the clinical decision support system developed using this model can facilitate personalized disease risk prediction, making it convenient for the clinical screening of a high-risk population and early disease intervention. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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12 pages, 1360 KiB

Open AccessArticle

Prognosis and Nomogram Prediction for Patients with Oral Squamous Cell Carcinoma: A Cohort Study

by Xin-Yuan Zhang, Shang Xie, Dian-Can Wang, Xiao-Feng Shan and Zhi-Gang Cai

Diagnostics 2023, 13(10), 1768; https://doi.org/10.3390/diagnostics13101768 - 17 May 2023

Cited by 2 | Viewed by 1138

Abstract

The TNM staging system is often used to predict the prognosis of patients with oral squamous cell carcinoma (OSCC). However, we have found that patients under the same TNM staging may exhibit tremendous differences in survival rates. Therefore, we aimed to investigate the prognosis of postoperative OSCC patients, establish a nomogram survival prediction model, and verify its effectiveness. Operative logs were reviewed for patients who underwent surgical treatment for OSCC at the Peking University School and Hospital of Stomatology. Patient demographic and surgical records were obtained, and they were followed up for overall survival (OS). A total of 432 patients with oral squamous cell carcinoma were included in the study, with a median follow-up time of 47 months. Based on the results of the Cox regression analysis, we constructed and verified the nomogram prediction model, which includes gender, BMI, OPMDs, pain score, SCC grade, and N stage. The C-index value of the 3-year and 5-year prediction models was 0.782 and 0.770, respectively, proving that the model has a certain level of prediction stability. The new nomogram prediction model has potential clinical significance for predicting the postoperative survival of OSCC patients. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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15 pages, 3076 KiB

Open AccessArticle

Modeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicity

by Zeynep Kucukakcali, Cemil Colak, Harika Gozde Gozukara Bag, Ipek Balikci Cicek, Onural Ozhan, Azibe Yildiz, Nefsun Danis, Ahmet Koc, Hakan Parlakpinar and Sami Akbulut

Diagnostics 2023, 13(9), 1583; https://doi.org/10.3390/diagnostics13091583 - 28 Apr 2023

Cited by 2 | Viewed by 1190

Abstract

Background: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. Methods: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. Results: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. Conclusions: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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14 pages, 9060 KiB

Open AccessArticle

Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study

by Tyler Wood, Justina O. Anigbo, George Eckert, Kelton T. Stewart, Mehmet Murat Dundar and Hakan Turkkahraman

Diagnostics 2023, 13(9), 1553; https://doi.org/10.3390/diagnostics13091553 - 26 Apr 2023

Cited by 7 | Viewed by 1186

Abstract

The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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14 pages, 1738 KiB

Open AccessArticle

Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence

by Sami Akbulut, Fatma Hilal Yagin, Ipek Balikci Cicek, Cemalettin Koc, Cemil Colak and Sezai Yilmaz

Diagnostics 2023, 13(6), 1173; https://doi.org/10.3390/diagnostics13061173 - 19 Mar 2023

Cited by 7 | Viewed by 1994

Abstract

Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6–90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6–94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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19 pages, 4910 KiB

Open AccessArticle

A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data

by Muhammet Fatih Aslan and Kadir Sabanci

Diagnostics 2023, 13(4), 796; https://doi.org/10.3390/diagnostics13040796 - 20 Feb 2023

Cited by 9 | Viewed by 2830

Abstract

Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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12 pages, 863 KiB

Open AccessArticle

Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis

by Dilber Uzun Ozsahin, Efe Precious Onakpojeruo, Berna Uzun, Mubarak Taiwo Mustapha and Ilker Ozsahin

Diagnostics 2023, 13(4), 618; https://doi.org/10.3390/diagnostics13040618 - 08 Feb 2023

Cited by 9 | Viewed by 2156

Abstract

The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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Review

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13 pages, 942 KiB

Open AccessReview

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease—The Future Is Now

by Claudia Diaconu, Monica State, Mihaela Birligea, Madalina Ifrim, Georgiana Bajdechi, Teodora Georgescu, Bogdan Mateescu and Theodor Voiosu

Diagnostics 2023, 13(4), 735; https://doi.org/10.3390/diagnostics13040735 - 15 Feb 2023

Cited by 4 | Viewed by 2285

Abstract

Crohn’s disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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18 pages, 1228 KiB

Open AccessReview

The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis

by Dilber Uzun Ozsahin, Declan Ikechukwu Emegano, Berna Uzun and Ilker Ozsahin

Diagnostics 2023, 13(1), 45; https://doi.org/10.3390/diagnostics13010045 - 23 Dec 2022

Cited by 13 | Viewed by 4185

Abstract

Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors’ clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction)

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