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Automated heart disease prediction using improved explainable learning-based technique

Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2024年 5卷 83-94页

作者： Huang, Yongrong Wang, Huiqing Chen, Zhide Feng, Chen Zhu, Kexin Yang, Xu Yang, Wencheng Fujian Normal University School of Computer and Cyberspace Security Fuzhou350117 China Department of Information Engineering Fuzhou Polytechnic Fuzhou350108 China Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan College of Computer and Data Science Minjiang University Fuzhou350108 China School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD4350 Australia

Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets. © 2020 IEEE.

关键词： Investments

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Spectral Normalized-Cut Graph Partitioning with Fairness Constraints 26

Spectral Normalized-Cut Graph Partitioning with Fairness Con...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Jia Wang, Yanhao Merchant, Arpit School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9781643684369

Normalized-cut graph partitioning aims to divide the set of nodes in a graph into k disjoint clusters to minimize the fraction of the total edges between any cluster and all other clusters. In this paper, we consider a fair variant of the partitioning problem wherein nodes are characterized by a categorical sensitive attribute (e.g., gender or race) indicating membership to different demographic groups. Our goal is to ensure that each group is approximately proportionally represented in each cluster while minimizing the normalized cut value. To resolve this problem, we propose a two-phase spectral algorithm called FNM. In the first phase, we add an augmented Lagrangian term based on our fairness criteria to the objective function for obtaining a fairer spectral node embedding. Then, in the second phase, we design a rounding scheme to produce k clusters from the fair embedding that effectively trades off fairness and partition quality. Through comprehensive experiments on nine benchmark datasets, we demonstrate the superior performance of FNM compared with three baseline methods. © 2023 The Authors.

关键词： Embeddings

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Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders

Machine Learning for Mental Health: A Systematic Study of Se...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Nadeem, Muhammad Rashid, Junaid Moon, Hyeonjoon Dosset, Arailym Sejong Uviversity Department of Computer Science and Engineering Seoul Korea Republic of Sejong University Department of Data Science Seoul Korea Republic of

ISBN: (纸本)9798350326413

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health. © 2023 IEEE.

关键词： Support vector machines

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Integrating Context and Criteria in Hotel Recommendations: A Deep Learning Perspective 1

Integrating Context and Criteria in Hotel Recommendations: A...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Raaj, Karan Murmu, Milind Kanalli, Shravya N Korgaokar, Tanush Vanahalli, Manjunath Prasad, Prabhu Sinha, Bam Bahadur IIIT Dharwad Electronics & Communication Engineering Karnataka 580001 India IIIT Dharwad Data Science & Artificial Intelligence Karnataka 580001 India IIIT Dharwad Computer Science & Engineering Karnataka 580001 India

ISBN: (纸本)9798350353648

Recommendation systems are widely utilized to personalize user experiences by sorting and providing information based on user preferences. Multi-Criteria Recommendation Systems (MCRs) enhance basic recommendation systems by considering user ratings and interactions across various criteria. When contextual information is also integrated, these systems become Context-Aware Multi-Criteria Recommendation Systems (CA-MCRs). This paper explores the use of Deep Neural Networks (DNN) in CA-MCRs for making predictions. The incorporation of deep learning in recommendation systems is relatively new and offers higher accuracy compared to traditional methods, as evaluated by RMSE and MAE scores. Deep Neural Networks (DNNs) facilitate efficient feature extraction through embedding layers, eliminating the need for manual intervention. The model is tested on TripAdvisor data, which includes multi-criteria ratings of various hotels. Our approach proved its supremacy by demonstrating the efficacy of deep learning in enhancing recommendation accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach 7

Navigating Complex Multiclass Classification in High-Dimensi...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Nemani, Praneeth Sundar Vadali, Venkata Surya Medi, Prathistith Raj Marisetty, Ashish Vollala, Satyanarayana Iiit Naya Raipur Department of Computer Science and Engineering India Iiit Naya Raipur Department of Data Science and Artificial Intelligence India

ISBN: (纸本)9798350343144

Large-scale tabular data classification is a critical task and the complexity arises from the vast amount of structured data generated in these fields, coupled with the challenges of high dimensionality and limited sample sizes. To address these challenges, advanced machine learning algorithms are required to analyze and categorize instances within these datasets effectively. In this work, we propose the usage of a state-of-the-art classifier called XBNet, which combines the strengths of tree-based classifiers and neural networks to tackle large tabular datasets. Our methodology is validated on a dataset with 64 dimensions and 11 classes, showcasing the model's capability to detect patterns and extract relevant features automatically. Furthermore, we employ K-Fold cross-validation to assess the model's performance, achieving an impressive training accuracy of 61.9% and a validation accuracy of 31.7%. These results surpass those of competing algorithms, confirming the superiority of our proposed methodology in handling large-scale tabular data classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

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Digital Communications and Networks 2024年第1期10卷 109-116页

作者： Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu The College of Computer and Information Hohai UniversityNanjingChina The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing China The School of Computer Science and Engineering Nanyang Technological UniversitySingapore The College of Textile and Clothing Engineering Soochow UniversitySuzhouChina The College of Internet of Things Nanjing University of Posts and TelecomurunicationsNanjingChina

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference ***,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable *** the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign ***,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power ***,agents in the collection stage slow down,which hinders the learning of other ***,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler *** experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

关键词： B5G Heterogeneous edge networks PPO Channel assignment Power allocation Throughput

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An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

An Empirical Analysis on Spatial Reasoning Capabilities of L...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs' spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our new benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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computers, Materials & Continua 2023年第3期74卷 6335-6349页

作者： Ibrar Amin Saima Hassan Samir Brahim Belhaouari Muhammad Hamza Azam Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan College of Science and Engineering Hamad bin Khalifa UniversityDohaQatar Centre of Research in Data Science Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Malaria is a lethal disease responsible for thousands of deaths worldwide every *** methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and *** automated diagnosis of diseases is progressively becoming *** deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical ***,labeling of medical images can be done with the help of medical experts *** recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising ***,the most common problem with these models is that they need a large amount of data for *** paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer *** proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning *** of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.

关键词： Generative adversarial network transfer learning semi-supervised malaria VGG16

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