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Comparison of machine learning algorithms for the classification of spinal cord tumor

IRISH JOURNAL OF MEDICAL SCIENCE 2024年第2期193卷 571-575页

作者： Garg, Sheetal Raghavan, Bhagyashree ATME Coll Engn Dept Elect & Commun Engn Mysuru India

Spinal cord Tumor has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into Bening or malignant has led many re- search teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, Logistic regression, Support Vector machines (SVMs), Decision Trees (DTs), Random forest classifier(RFs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we have discussed a predictive model based on various supervised ML techniques.

关键词： KNN Logistic regression machine learning algorithms MRI Random forest Spinal cord tumor Support vector machine

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Mental Workload Estimation from EEG Signals Using machine learning algorithms 1

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15th International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE) held as part of the 20th International Conference on Human-Computer Interaction (HCI International)

作者： Cheema, Baljeet Singh Samima, Shabnam Sarma, Monalisa Samanta, Debasis Minist Def Army Govt India Integrated Headquarters Directorate Gen Informat Syst Kharagpur W Bengal India Indian Inst Technol Kharagpur Subir Chowdhury Sch Qual & Reliabil Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (数字)9783319911229

ISBN: (纸本)9783319911229;9783319911212

Multitasking conditions prevalent in many environments such as critical operations in defense activities, evaluating user interfaces in man-machine interaction, etc. require assessment of mental workload of operators. However, mental workload (MWL) cannot be perceived directly as it is a complex and abstract property of human physiology. The techniques available in the literature for its assessment usually depend on subjective analysis, performance analysis and psycho-physiological measurements. But, these approaches despite being followed often prove to be inadequate due to high inter-personal variations and inconvenient procedures and subjective to the bias of evaluators. With the recent advancements of proliferation of Brain Computer Interface (BCI) devices and machine learning algorithms, it is possible to estimate MWL automatically. Nevertheless, there is a need to address issue like managing a high dimension and high volume data in real time. In this work, we propose an approach to estimate the mental workload using electroencephalogram (EEG) signals of an operator while in operation. A thorough investigation of different features, optimization of features and selecting an optimal number of channels are the some of the crucial steps have been addressed in this work. We propose a novel feature engineering method to extract a reduced set of features and utilize only a sub-set of channels for the purpose of classification of workload into different levels with the help of supervised machine learning techniques. Further, we investigate the performance of different classifiers and compare their results. It can be inferred from the observed results that mental workload estimation using machine learning algorithms is a better solution compared to the existing approaches.

关键词： Electroencephalography Psycho-physiological measurement Brain-computer interface Mental workload estimation machine learning algorithms n-back task Dual n-back task

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Prediction of Flooding Due to Heavy Rainfall in India Using machine learning algorithms: Providing Advanced Warning

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IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 2022年第4期8卷 26-33页

作者： Balamurugan, R. Choudhary, Kshitiz Raja, S. P. Vellore Inst Technol Commuter Sci & Engn Vellore 632014 Tamil Nadu India Vellore Inst Technol Vellore 632014 Tamil Nadu India

Floods are one of the deadliest disasters in the coastal areas of India. Consistently, flood, the most widely recognized catastrophe in India, has an enormous effect on the nation’s property and lives. Therefore, this article is focused on developing an effective flood-prediction system using machine learning (ML) algorithms that can help with preventing the loss of human lives and property. We will use k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs), and decision trees (DTs) to build our ML models. And to resolve the issue of oversampling and low accuracy, a stacking classifier will be used. For comparison among these models, we will use accuracy, f1-scores, recall, and precision. The results indicate that stacked models are best for predicting floods due to real-time rainfall in that area. It is noted that Andhra Pradesh achieves the highest accuracy of 97.91%, whereas Orissa achieves an accuracy of 92.36%, lowest among the eight coastal states.

关键词： Support vector machines machine learning algorithms Precipitation Stacking Sea measurements Predictive models Real-time systems Floods

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Detection of spectrum hole from n-number of primary users using machine learning algorithms

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JOURNAL OF ENGINEERING-JOE 2020年第5期2020卷 175-188页

作者： Venkateshkumar, Udayamoorthy Ramakrishnan, Srinivansan Sri Krishna Coll Technol Dept ECE Coimbatore 641042 Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept IT Pollachi 642003 Tamil Nadu India

A method for detecting spectrum holes based on the n-number of primary users (PU's) in a cognitive radio environment, using a cooperative spectrum sensing model is proposed in this study. The fusion centre, senses the n-number of PUs. When the number of PUs is >200, the probability of detection decreases, while the probability of a false alarm increases. The authors use the random forest (RF) algorithm to classify a customised dataset of 600 training samples. Further, they compare the RF algorithm and the k-means clustering algorithm, using test datasets with a minimum of ten PUs and a maximum of 500 PUs. Five different signal features are considered as the attributes in the proposed model. The maximum probability of detection is achieved using the k-means clustering algorithm in the case of 200 PUs and is 99.17%, while the false alarm probability is 0.8%. The receiver operating characteristic curves indicated that probability of detecting a spectrum hole in the case of the dataset with 500 PUs is 97.67% with the signal to noise ratio ranging from 10 to -12 dB. The accuracy can be increased if the number of clusters formed is increased, depending on the number of test samples.

关键词： cognitive radio signal detection radio spectrum management probability learning (artificial intelligence) spectrum hole primary users machine learning algorithms PU cognitive radio environment spectrum sensing model detection decreases false alarm increases random forest algorithm RF algorithm clustering algorithm 500 PUs maximum probability 200 PUs false alarm probability noise figure -12 0 dB to 10 0 dB

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Health Data-Driven machine learning algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease

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RISK MANAGEMENT AND HEALTHCARE POLICY 2021年 14卷 4401-4412页

作者： Chiu, Yen-Ling Jhou, Mao-Jhen Lee, Tian-Shyug Lu, Chi-Jie Chen, Ming-Shu Yuan Ze Univ Grad Inst Med Taoyuan 32003 Taiwan Yuan Ze Univ Grad Sch Biomed Informat Taoyuan 32003 Taiwan Natl Taiwan Univ Coll Med Grad Inst Clin Med Taipei 10002 Taiwan Far Eastern Mem Hosp Dept Med Res Dept Med New Taipei 22056 Taiwan Fu Jen Catholic Univ Grad Inst Business Adm New Taipei 242062 Taiwan Fu Jen Catholic Univ Artificial Intelligence Dev Ctr New Taipei 242062 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei 242062 Taiwan Asia Eastern Univ Sci & Technol Coll Healthcare & Management Dept Healthcare Adm New Taipei 22061 Taiwan

Purpose: As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations. Methods: This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors. Results: The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socio-economic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods. Conclusion: The five prediction model methods can provid

关键词： chronic kidney disease health screening machine learning algorithms risk indicators assessment education

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Discrimination of mesophilic and thermophilic proteins using machine learning algorithms

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2008年第4期70卷 1274-1279页

作者： Gromiha, M. Michael Suresh, M. Xavier Natl Inst Adv Ind Sci & Technol Computat Biol Res Ctr Koto Ku Tokyo 1350064 Japan

Discriminating thermophilic proteins from their mesophilic counterparts is a challenging task and it would help to design stable proteins. In this work, we have systematically analyzed the amino acid compositions of 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively. We found that the charged residues Lys, Arg, and Glu as well as the hydrophobic residues, Val and Ile have higher occurrence in thermophiles than mesophiles. Further, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees and so forth for discriminating mesophilic and thermophilic proteins. We found that most of the machine learning techniques discriminate these classes of proteins with similar accuracy. The neural network-based method could discriminate the thermophiles from mesophiles at the five-fold cross-validation accuracy of 89% in a dataset of 4684 proteins. Moreover, this method is tested with 325 mesophiles in Xylella fastidosa and 382 thermophiles in Aquifex aeolicus and it could successfully discriminate them with the accuracy of 91%. These accuracy levels are better than other methods in the literature and we suggest that this method could be effectively used to discriminate mesophilic and thermophilic proteins.

关键词： protein stability machine learning algorithms neural networks mesophilic thermophilic

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Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using machine learning algorithms

Inertial Sensor Based Modelling of Human Activity Classes: F...

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International Summit on eHealth (eHealth360)

作者： Zebin, Tahmina Scully, Patricia J. Ozanyan, Krikor B. Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England Univ Manchester Photon Sci Inst Manchester M13 9PL Lancs England

ISBN: (纸本)9783319496559;9783319496542

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance.

关键词： Inertial measurement unit Accelerometer data Feature extraction Data-fusion machine learning algorithms Human Activity Recognition

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machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

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HELIYON 2024年第4期10卷 e26652页

作者： Altman, Morteza Babaee Wan, Wenbin Hosseini, Amineh Sadat Nowdeh, Saber Arabi Alizadeh, Masoumeh Amirkabir Univ Technol Tehran Polytech Dept Energy Engn Tehran *** Iran Univ New Mexico Dept Mech Engn MSC01 1150 Albuquerque NM 87131 USA Islamic Azad Univ Dept Elect & Biomed Engn Gorgan Golestan Iran Golestan Tech & Vocat Training Ctr Gorgan Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran *** Iran

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast machine learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications.

关键词： Biomedical applications Embedded System FPGA Hardware acceleration machine learning algorithms

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machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease

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EUROPEAN JOURNAL OF INTEGRATIVE MEDICINE 2021年 43卷 101288-101288页

作者： Fan, Shangyong Chen, Bin Zhang, Xirui Hu, Xiaojuan Bao, Lingshan Yang, Xiaodong Liu, Zhaobang Yu, Yingcong Yangzhou Univ Dept Coll Informat Engn Yangzhou 22500 Jiangsu Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Innovat Ctr TCM Hlth Serv Shanghai 201203 Peoples R China Wenzhou Med Univ Wenzhou Peoples Hosp Dept Gastroenterol Wenzhou 3 Clin Inst Wenzhou 325000 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Dept Med Imaging Suzhou 215163 Peoples R China

Introduction: Tongue diagnosis in Traditional Chinese Medicine (TCM) is used in clinical practice for disease diagnosis but this may be perceived as being too subjective. Objective technological approaches can lead to more accurate diagnosis and have become a focus for research. The aim of this study was to explore different tongue features in people with diabetes, gastric symptoms using images collected using digital tongue imaging. Methods: machine learning algorithms were used to classify diabetes mellitus(DM) patients and healthy participants, and patients exhibiting two different TCM gastric disease symptoms (liver-stomach disharmony and spleen-stomach deficiency). Multi-type feature extraction and selection from 466 tongue images was conducted. In the feature extraction stage, texture features and four TCM tongue features were identified: constitution color, coating color, cracks, and plumpness and slenderness. In the classification stage, two different classification algorithms were employed, Random Forest and Support Vector machine, to classify DM and TCM gastric disease symptoms. Results: Regarding the classification results, the area under the receiver operating characteristic curve (AUC) reached 97.9% +/- 0.1%, with maximum sensitivity and specificity of 93.5% +/- 0.6% and 95.0% +/- 0.8%, for DM patients and healthy volunteers respectively. For the classification of TCM gastric disease symptoms, the highest AUC was only 69.4% +/- 2.6%, with a sensitivity and specificity of 65.2% +/- 2.9% and 63.7% +/- 2.1%, respectively. Conclusions: Experiments shows that the TCM tongue features (color, cracks, plumpness and slenderness) and texture features have good performance for disease diagnosis. However, classification of DM was significantly better than for TCM gastric symptoms. The RF classification model was also significantly better than the SVM classifier model.

关键词： Tongue diagnosis Traditional Chinese Medicine Diabetes mellitus TCM Syndrome Gastric disease machine learning algorithms

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machine learning algorithms for Document Classification: Comparative Analysis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第4期13卷 260-265页

作者： Rashid, Faizur Gargaare, Suleiman M. A. Aden, Abdulkadir H. Abdi, Afendi Haramaya Univ Dept Comp Sci Almaya Ethiopia Univ Hargeisa Dept Comp Sci Hargeisa Somalia Bule Hora Univ Dept Comp Sci Bule Hora Ethiopia Haramaya Univ Dept Software Engn Almaya Ethiopia

Automated document classification is the machine learning fundamental that refers to assigning automatic categories among scanned images of the documents. It reached the state-of-art stage but it needs to verify the performance and efficiency of the algorithm by comparing. The objective was to get the most efficient classification algorithms according to the usage of the fundamentals of science. Experimental methods were used by collecting data from a sum of 1080 students and researchers from Ethiopian universities and a meta-data set of Banknotes, Crowdsourced Mapping, and VxHeaven provided by UC Irvine. 25% of the respondents felt that KNN is better than the other models. The overall analysis of performance accuracies through various parameters namely accuracy percentage of 99.85%, the precision performance of 0.996, recall ratio of 100%, F-Score 0.997, classification time, and running time of KNN, SVM, Perceptron and Gaussian NB was observed. KNN performed better than the other classification algorithms with a fewer error rate of 0.0002 including the efficiency of the least classification time and running time with similar to 413 and 3.6978 microseconds consecutively. It is concluded by looking at all the parameters that KNN classifiers have been recognized as the best algorithm.

关键词： Document classification machine learning algorithms text classification analysis

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