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15 pages, 1395 KiB

Open AccessArticle

Application of Artificial Neural Network to Somatotype Determination

by Małgorzata Drywień, Krzysztof Górnicki and Magdalena Górnicka

Appl. Sci. 2021, 11(4), 1365; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041365 - 03 Feb 2021

Cited by 5 | Viewed by 2575

Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for [...] Read more.

Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), ectomorphy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath–Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, χ² = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, χ² = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and χ² = 0.51. The bioelectrical impedance analysis and Heath–Carter method compliance was evaluated with the statistical algorithm proposed by Bland and Altman. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women’s ratings with high accuracy and agreement with the Heath–Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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18 pages, 1759 KiB

Open AccessArticle

Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

by Alhanoof Althnian, Duaa AlSaeed, Heyam Al-Baity, Amani Samha, Alanoud Bin Dris, Najla Alzakari, Afnan Abou Elwafa and Heba Kurdi

Appl. Sci. 2021, 11(2), 796; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020796 - 15 Jan 2021

Cited by 136 | Viewed by 12445

Abstract

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six [...] Read more.

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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24 pages, 8650 KiB

Open AccessArticle

Machine Learning Methods with Decision Forests for Parkinson’s Detection

by Moumita Pramanik, Ratika Pradhan, Parvati Nandy, Akash Kumar Bhoi and Paolo Barsocchi

Appl. Sci. 2021, 11(2), 581; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020581 - 08 Jan 2021

Cited by 33 | Viewed by 3084

Abstract

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve [...] Read more.

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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17 pages, 4465 KiB

Open AccessArticle

Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification

by Chun-Hui Lin, Cheng-Jian Lin, Yu-Chi Li and Shyh-Hau Wang

Appl. Sci. 2021, 11(2), 480; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020480 - 06 Jan 2021

Cited by 16 | Viewed by 2748

Abstract

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning [...] Read more.

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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12 pages, 3543 KiB

Open AccessEditor’s ChoiceArticle

Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning

by Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ryu Matsuoka, Suguru Yasutomi, Kanto Shozu, Ai Dozen, Hidenori Machino, Hirokazu Hidaka, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Akihiko Sekizawa and Ryuji Hamamoto

Appl. Sci. 2021, 11(1), 371; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010371 - 02 Jan 2021

Cited by 61 | Viewed by 9455

Abstract

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences [...] Read more.

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of “explainable AI.” Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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23 pages, 1007 KiB

Open AccessArticle

Ensemble Learning for Skeleton-Based Body Mass Index Classification

by Beom Kwon and Sanghoon Lee

Appl. Sci. 2020, 10(21), 7812; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217812 - 04 Nov 2020

Cited by 3 | Viewed by 3228

Abstract

In this study, we performed skeleton-based body mass index (BMI) classification by developing a unique ensemble learning method for human healthcare. Traditionally, anthropometric features, including the average length of each body part and average height, have been utilized for this kind of classification. [...] Read more.

In this study, we performed skeleton-based body mass index (BMI) classification by developing a unique ensemble learning method for human healthcare. Traditionally, anthropometric features, including the average length of each body part and average height, have been utilized for this kind of classification. Average values are generally calculated for all frames because the length of body parts and the subject height vary over time, as a result of the inaccuracy in pose estimation. Thus, traditionally, anthropometric features are measured over a long period. In contrast, we controlled the window used to measure anthropometric features over short/mid/long-term periods. This approach enables our proposed ensemble model to obtain robust and accurate BMI classification results. To produce final results, the proposed ensemble model utilizes multiple k-nearest neighbor classifiers trained using anthropometric features measured over several different time periods. To verify the effectiveness of the proposed model, we evaluated it using a public dataset. The simulation results demonstrate that the proposed model achieves state-of-the-art performance when compared with benchmark methods. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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Review

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18 pages, 3738 KiB

Open AccessReview

Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process

by Ľudmila Pusztová, František Babič and Ján Paralič

Appl. Sci. 2021, 11(1), 292; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010292 - 30 Dec 2020

Cited by 3 | Viewed by 2333

Abstract

The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates [...] Read more.

The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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28 pages, 917 KiB

Open AccessReview

A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis

by Domagoj Marijanović and Damir Filko

Appl. Sci. 2020, 10(21), 7613; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217613 - 28 Oct 2020

Cited by 19 | Viewed by 6388

Abstract

Chronic wounds or wounds that are not healing properly are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be [...] Read more.

Chronic wounds or wounds that are not healing properly are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. Two major tasks in wound analysis on images are segmentation of the wound from the healthy skin and background, and classification of the most important wound tissues like granulation, fibrin, and necrosis. These tasks are necessary for further assessment like wound measurement or healing evaluation based on tissue representation. Researchers use various methods and algorithms for image wound analysis with the aim to outperform accuracy rates and show the robustness of the proposed methods. Recently, neural networks and deep learning algorithms have driven considerable performance improvement across various fields, which has a led to a significant rise of research papers in the field of wound analysis as well. The aim of this paper is to provide an overview of recent methods for non-contact wound analysis which could be used for developing an end-to-end solution for a fully automated wound analysis system which would incorporate all stages from data acquisition, to segmentation and classification, ending with measurement and healing evaluation. Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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