BioMedInformatics最新文献

{"title":"Real-Time Jaundice Detection in Neonates Based on Machine Learning Models","authors":"Ahmad Yaseen Abdulrazzak, Saleem Latif Mohammed, Ali Al-Naji, J. Chahl","doi":"10.3390/biomedinformatics4010034","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010034","url":null,"abstract":"Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"22 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approach to Identify Case-Control Studies on ApoE Gene Mutations Linked to Alzheimer’s Disease in Italy","authors":"Giorgia Francesca Saraceno, Diana Marisol Abrego-Guandique, Roberto Cannataro, M. Caroleo, E. Cione","doi":"10.3390/biomedinformatics4010033","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010033","url":null,"abstract":"Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in the identification and screening of papers present in the literature that focus on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer’s Disease patients. Results: MySLR excludes duplicates and creates topics. MySLR was applied to analyze a set of 164 scientific publications. After duplicate removal, the results allowed us to identify 92 papers divided into two relevant topics characterizing the investigated research area. Topic 1 contains 70 papers, and topic 2 contains the remaining 22. Despite the current limitations, the available evidence suggests that articles containing studies on Italian Alzheimer’s Disease (AD) patients were 65.22% (n = 60). Furthermore, the presence of papers about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, the primary genetic risk factor of AD, for the Italian population was 5.4% (n = 5). Conclusion: The results show that the machine learning platform helped to identify case-control studies on ApoE gene mutations, including SNPs, but not only conducted in Italy.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Practical Applications of Computational Intelligence Technology","authors":"Y. Matsuzaka, R. Yashiro","doi":"10.3390/biomedinformatics4010032","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010032","url":null,"abstract":"Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"64 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140439537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones","authors":"Michele Giuseppe Di Cesare, D. Perpetuini, D. Cardone, A. Merla","doi":"10.3390/biomedinformatics4010031","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010031","url":null,"abstract":"Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"140 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications","authors":"M. Gromiha, Palanisamy Preethi, Medha Pandey","doi":"10.3390/biomedinformatics4010030","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010030","url":null,"abstract":"Artificial intelligence (AI), a branch of computer science, involves developing intelligent computer programs to mimic human intelligence and automate various processes [...]","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"132 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?","authors":"Eleanor Jenkinson, Ognjen Arandjelovíc","doi":"10.3390/biomedinformatics4010028","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010028","url":null,"abstract":"Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records","authors":"Alae Eddine El Hmimdi, Zoï Kapoula, Vivien Sainte Fare Garnot","doi":"10.3390/biomedinformatics4010029","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010029","url":null,"abstract":"Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"13 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?","authors":"Eleanor Jenkinson, Ognjen Arandjelovíc","doi":"10.3390/biomedinformatics4010028","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010028","url":null,"abstract":"Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records","authors":"Alae Eddine El Hmimdi, Zoï Kapoula, Vivien Sainte Fare Garnot","doi":"10.3390/biomedinformatics4010029","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010029","url":null,"abstract":"Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"45 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted Rank Difference Ensemble: A New Form of Ensemble Feature Selection Method for Medical Datasets","authors":"Arju Manara Begum, M. Mondal, Prajoy Podder, J. Kamruzzaman","doi":"10.3390/biomedinformatics4010027","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010027","url":null,"abstract":"Background: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called weighted rank difference ensemble (WRD-Ensemble) has been put forth. It combines three FS methods to produce a stable and diverse subset of features. The three base FS approaches are Pearson’s correlation coefficient (PCC), reliefF, and gain ratio (GR). These three FS approaches produce three distinct lists of features, and then they order each feature by importance or weight. The final subset of features in this study is chosen using the average weight of each feature and the rank difference of a feature across three ranked lists. Using the average weight and rank difference of each feature, unstable and less significant features are eliminated from the feature space. The WRD-Ensemble method is applied to three medical datasets: chronic kidney disease (CKD), lung cancer, and heart disease. These data samples are classified using logistic regression (LR). Results: The experimental results show that compared to the base FS methods and other ensemble FS methods, the proposed WRD-Ensemble method leads to obtaining the highest accuracy value of 98.97% for CKD, 93.24% for lung cancer, and 83.84% for heart disease. Conclusion: The results indicate that the proposed WRD-Ensemble method can potentially improve the accuracy of disease diagnosis models, contributing to advances in clinical decision-making.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"12 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}