PeerJ Computer Science最新文献_第5页

An adaptive method for determining the optimal number of topics in topic modeling. 在主题建模中确定最佳主题数量的自适应方法。

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-28 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2723

Yang Xu, Yueyi Zhang, Yefang Sun, Hanting Zhou

{"title":"An adaptive method for determining the optimal number of topics in topic modeling.","authors":"Yang Xu, Yueyi Zhang, Yefang Sun, Hanting Zhou","doi":"10.7717/peerj-cs.2723","DOIUrl":"10.7717/peerj-cs.2723","url":null,"abstract":"Topic models have been successfully applied to information classification and retrieval. The difficulty in successfully applying these technologies is to select the appropriate number of topics for a given corpus. Selecting too few topics can result in information loss and topic omission, known as underfitting. Conversely, an excess of topics can introduce noise and complexity, resulting in overfitting. Therefore, this article considers the inter-class distance and proposes a new method to determine the number of topics based on clustering results, named average inter-class distance change rate (AICDR). AICDR employs the Ward's method to calculate inter-class distances, then calculates the average inter-class distance for different numbers of topics, and determines the optimal number of topics based on the average distance change rate. Experiments show that the number of topics determined by AICDR is more in line with the true classification of datasets, with high inter-class distance and low inter-class similarity, avoiding the phenomenon of topic overlap. AICDR is a technique predicated on clustering results to select the optimal number of topics and has strong adaptability to various topic models.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2723"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive machine learning approaches utilizing soft decision-making via intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. 通过直观模糊参数化直观模糊软矩阵利用软决策的自适应机器学习方法。

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-28 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2703

Samet Memiş, Ferhan Şola Erduran, Hivda Aydoğan

{"title":"Adaptive machine learning approaches utilizing soft decision-making via intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices.","authors":"Samet Memiş, Ferhan Şola Erduran, Hivda Aydoğan","doi":"10.7717/peerj-cs.2703","DOIUrl":"10.7717/peerj-cs.2703","url":null,"abstract":"The exponential data growth generated by technological advancements presents significant challenges in analysis and decision-making, necessitating innovative and robust methodologies. Machine learning has emerged as a transformative tool to address these challenges, especially in scenarios requiring precision and adaptability. This study introduces two novel adaptive machine learning approaches, i.e., AIFPIFSC1 and AIFPIFSC2. These methods leverage the modeling ability of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices). This state-of-the-art framework enhances the classification task in machine learning by employing soft decision-making through ifpifs-matrices. The proposed approaches are rigorously evaluated against leading fuzzy/soft-based classifiers using 15 widely recognized University of California, Irvine datasets, including accuracy and robustness, across six performance metrics. Statistical analyses conducted using Friedman and Nemenyi tests further substantiate the reliability and superiority of the proposed approaches. The results consistently demonstrate that these approaches outperform their counterparts, highlighting their potential for solving complex classification problems. This study contributes to the field by offering adaptable and effective solutions for modern data analysis challenges, paving the way for future advancements in machine learning and decision-making systems.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2703"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimal peer selection for peer-to-peer video content distribution using fuzzy linear programming approach.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-28 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2701

M Anandaraj, Naganandhini S

{"title":"An optimal peer selection for peer-to-peer video content distribution using fuzzy linear programming approach.","authors":"M Anandaraj, Naganandhini S","doi":"10.7717/peerj-cs.2701","DOIUrl":"10.7717/peerj-cs.2701","url":null,"abstract":"The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework is introduced that uses fuzzy linear programming (FLP) to address the inherent uncertainties in peer selection. The primary motivation for the use of FLP lies in its capability to handle the imprecision and vagueness that are characteristic of dynamic P2P environments. Factors such as peer reliability, bandwidth, and proximity are often uncertain in this environment. By using fuzzy logic, the proposed framework models these criteria as fuzzy sets and then integrates uncertainty into the decision-making process. FLP is then applied to optimize peer selection, improving download speed, reducing download time, and enhancing peer reliability. The proposed method is evaluated and analyzed using extensive simulation with SciPy. The result reveals that proposed technique works better compared to some of the traditional methods in terms of download time, download speed and also reliability measure. It also exhibits approximately 20% of increase in download speed as well as a 15% decrease in download time compared to traditional approaches. It leads to faster content retrieval and enhanced the efficiency in content distribution. Also, in selection of reliable peers for content distribution, there is a notable 20% of increase in peer reliability with result of enhanced robustness. The proposed method provides efficient and robust solution to the problem of peer selection. It can be implemented in a broad range of P2P content distribution networks.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2701"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Atom Search Optimization: a comprehensive review of its variants, applications, and future directions.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-28 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2722

Mohammed A El-Shorbagy, Anas Bouaouda, Laith Abualigah, Fatma A Hashim

{"title":"Atom Search Optimization: a comprehensive review of its variants, applications, and future directions.","authors":"Mohammed A El-Shorbagy, Anas Bouaouda, Laith Abualigah, Fatma A Hashim","doi":"10.7717/peerj-cs.2722","DOIUrl":"10.7717/peerj-cs.2722","url":null,"abstract":"The Atom Search Optimization (ASO) algorithm is a recent advancement in metaheuristic optimization inspired by principles of molecular dynamics. It mathematically models and simulates the natural behavior of atoms, with interactions governed by forces derived from the Lennard-Jones potential and constraint forces based on bond-length potentials. Since its inception in 2019, it has been successfully applied to various challenges across diverse fields in technology and science. Despite its notable achievements and the rapidly growing body of literature on ASO in the metaheuristic optimization domain, a comprehensive study evaluating the success of its various implementations is still lacking. To address this gap, this article provides a thorough review of half a decade of advancements in ASO research, synthesizing a wide range of studies to highlight key ASO variants, their foundational principles, and significant achievements. It examines diverse applications, including single- and multi-objective optimization problems, and introduces a well-structured taxonomy to guide future exploration in ASO-related research. The reviewed literature reveals that several variants of the ASO algorithm, including modifications, hybridizations, and multi-objective implementations, have been developed to tackle complex optimization problems. Moreover, ASO has been effectively applied across various domains, such as engineering, healthcare and medical applications, Internet of Things and communication, clustering and data mining, environmental modeling, and security, with engineering emerging as the most prevalent application area. By addressing the common challenges researchers face in selecting appropriate algorithms for real-world problems, this study provides valuable insights into the practical applications of ASO and offers guidance for designing ASO variants tailored to specific optimization problems.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2722"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain and explainable-AI integrated system for Polycystic Ovary Syndrome (PCOS) detection.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-28 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2702

Gowthami Jaganathan, Shanthi Natesan

{"title":"Blockchain and explainable-AI integrated system for Polycystic Ovary Syndrome (PCOS) detection.","authors":"Gowthami Jaganathan, Shanthi Natesan","doi":"10.7717/peerj-cs.2702","DOIUrl":"10.7717/peerj-cs.2702","url":null,"abstract":"In the modern era of digitalization, integration with blockchain and machine learning (ML) technologies is most important for improving applications in healthcare management and secure prediction analysis of health data. This research aims to develop a novel methodology for securely storing patient medical data and analyzing it for PCOS prediction. The main goals are to leverage Hyperledger Fabric for immutable, private data and to integrate Explainable Artificial Intelligence (XAI) techniques to enhance transparency in decision-making. The innovation of this study is the unique integration of blockchain technology with ML and XAI, solving critical issues of data security and model interpretability in healthcare. With the Caliper tool, the Hyperledger Fabric blockchain's performance is evaluated and enhanced. The suggested Explainable AI-based blockchain system for Polycystic Ovary Syndrome detection (EAIBS-PCOS) system demonstrates outstanding performance and records 98% accuracy, 100% precision, 98.04% recall, and a resultant F1-score of 99.01%. Such quantitative measures ensure the success of the proposed methodology in delivering dependable and intelligible predictions for PCOS diagnosis, therefore making a great addition to the literature while serving as a solid solution for healthcare applications in the near future.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2702"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A secure healthcare data transmission based on synchronization of fractional order chaotic systems.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2665

Nur Afiqah Suzelan Amir, Fatin Nabila Abd Latiff, Kok Bin Wong, Wan Ainun Mior Othman

{"title":"A secure healthcare data transmission based on synchronization of fractional order chaotic systems.","authors":"Nur Afiqah Suzelan Amir, Fatin Nabila Abd Latiff, Kok Bin Wong, Wan Ainun Mior Othman","doi":"10.7717/peerj-cs.2665","DOIUrl":"10.7717/peerj-cs.2665","url":null,"abstract":"The transmission of healthcare data plays a vital role in cities worldwide, facilitating access to patient's health information across healthcare systems and contributing to the enhancement of care services. Ensuring secure healthcare transmission requires that the transmitted data be reliable. However, verifying this reliability can potentially compromise patient privacy. Given the sensitive nature of health information, preserving privacy remains a paramount concern in healthcare systems. In this work, we present a novel secure communication scheme that leverages a chaos cryptosystem to address the critical concerns of reliability and privacy in healthcare data transmission. Chaos-based cryptosystems are particularly well-suited for such applications due to their inherent sensitivity to initial conditions, which significantly enhances resistance to adversarial violations. This property makes the chaos-based approach highly effective in ensuring the security of sensitive healthcare data. The proposed chaos cryptosystem in this work is built upon the synchronization of fractional-order chaotic systems with varying structures and orders. The synchronization between the primary system (PS) and the secondary system (SS) is achieved through the application of Lyapunov stability theory. For the encryption and decryption of sensitive healthcare data, the scheme employs the n-shift encryption principle. Furthermore, a detailed analysis of the key space was conducted to ensure the scheme's robustness against potential attacks. Numerical simulations were also performed to validate the effectiveness of the proposed scheme.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2665"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2719

Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu

{"title":"Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns.","authors":"Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu","doi":"10.7717/peerj-cs.2719","DOIUrl":"10.7717/peerj-cs.2719","url":null,"abstract":"This study discusses using Japanese candlestick (JC) patterns to predict future price movements in financial markets. The history of candlestick trading dates back to the 17th century and involves the analysis of patterns formed during JC trading. Candlestick patterns are practical tools for the technical analysis of traders in financial markets. They may serve as indicators of traders' documents of a potential change in market sentiment and trend direction. This study aimed to predict the following candle-trend-based JC charts using convolutional neural networks (CNNs). In order to enhance the accuracy of predicting the directional movement of subsequent financial candlesticks, a rich dataset has been constructed by following a structured three-step process, and a CNN model has been trained. Initially, the dataset was analyzed, and sub-charts were generated using a sliding window technique. Subsequently, the Ta-lib library was used to identify whether predefined patterns were present within the windows. The third phase involved the classification of each window's directional tendency, which was substantiated by employing various technical indicators to validate the direction of the trend. Following the data preparation and analysis phases, a CNN model was developed to extract features from sub-charts and facilitate precise predictions effectively. The experimental results of this approach demonstrated a remarkable predictive accuracy of up to 99.3%. Implementing cross-validation techniques is essential to verify the reliability and overall performance of the model. To achieve this goal, the dataset was divided into several small subsets. Subsequently, the model was trained and evaluated multiple times using different combinations of these subsets. This method allows for a more accurate assessment of the model's predictive capabilities by examining its performance on unseen data.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2719"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of perinatal depression among women in Pakistan using Hybrid RNN-LSTM model.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-26 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2673

Amna Zafar, Muhammad Wasim, Beenish Ayesha Akram, Maham Riaz, Ivan Miguel Pires, Paulo Jorge Coelho

{"title":"Prediction of perinatal depression among women in Pakistan using Hybrid RNN-LSTM model.","authors":"Amna Zafar, Muhammad Wasim, Beenish Ayesha Akram, Maham Riaz, Ivan Miguel Pires, Paulo Jorge Coelho","doi":"10.7717/peerj-cs.2673","DOIUrl":"10.7717/peerj-cs.2673","url":null,"abstract":"Perinatal depression (PND) refers to a complex mental health condition that can occur during pregnancy (prenatal period) or in the first year after childbirth (postnatal period). Prediction of PND holds considerable importance due to its significant role in safeguarding the mental health and overall well-being of both mothers and their infants. Unfortunately, PND is difficult to diagnose at an early stage and thus may elevate the risk of suicide during pregnancy. In addition, it contributes to the development of postnatal depressive disorders. Despite the gravity of the problem, the resources for developing and training AI models in this area remain limited. To this end, in this work, we have locally curated a novel dataset named PERI DEP using the Patient Health Questionnaire (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS), and socio-demographic questionnaires. The dataset consists of 14,008 records of women who participated in the hospitals of Lahore and Gujranwala regions. We have used SMOTE and GAN oversampling for data augmentation on the training set to solve the class imbalance problem. Furthermore, we propose a novel deep-learning framework combining the recurrent neural networks (RNN) and long short-term memory (LSTM) architectures. The results indicate that our hybrid RNN-LSTM model with SMOTE augmentation achieves a higher accuracy of 95% with an F1 score of 96%. Our study reveals the prevalence rate of PND among women in Pakistan (73.1%) indicating the need to prioritize the prevention and intervention strategies to overcome this public health challenge.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2673"},"PeriodicalIF":3.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated evaluation systems to enhance exam quality and reduce test anxiety.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2666

Doaa Mohamed Elbourhamy

{"title":"Automated evaluation systems to enhance exam quality and reduce test anxiety.","authors":"Doaa Mohamed Elbourhamy","doi":"10.7717/peerj-cs.2666","DOIUrl":"10.7717/peerj-cs.2666","url":null,"abstract":"University examination papers play a crucial role in the institution's quality, impacting the institution's accreditation status. In this context, ensuring the quality of examination papers is paramount. In practice, however, manual assessments are mostly laborious and time-consuming and generally lack consistency. The last decade has seen digital education acquire immense interest in academic discourse, especially when developing intelligent systems for educational assessment. The presented work proposes an automated system that allows text analysis and evaluation of university exam papers by formal and technical criteria. The research was conducted by analyzing 30 exam papers, which will be included in each of the exam papers, which consist of 60 questions each, in total it holds 1,800 questions. Moreover, it also includes research to understand the quality and relationship with students' test anxiety. A total of 50 year one first-year students were taken to measure students' academic stress by a scale. Planning on basic levels and adherence to technical standards were missing in the exam papers. The proposed automated system has improved exam paper quality to a great extent and reduced academic stress among students with an accuracy of 98% in identifying and matching specified criteria.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2666"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable multi-objective hybrid machine learning model for reducing heart failure mortality. 降低心力衰竭死亡率的可解释多目标混合机器学习模型。

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.2682

F M Javed Mehedi Shamrat, Majdi Khalid, Thamir M Qadah, Majed Farrash, Hanan Alshanbari

{"title":"An explainable multi-objective hybrid machine learning model for reducing heart failure mortality.","authors":"F M Javed Mehedi Shamrat, Majdi Khalid, Thamir M Qadah, Majed Farrash, Hanan Alshanbari","doi":"10.7717/peerj-cs.2682","DOIUrl":"10.7717/peerj-cs.2682","url":null,"abstract":"As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on a global scale. The most effective approach to improving the chances of individuals' survival is to diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms to achieve the goal. However, these approaches may not consistently demonstrate robust performance when applied to data that they have not encountered before, and struggle to discern intricate patterns within the data. Hence, we present a Multi-objective Stacked Enable Hybrid Model (MO-SEHM), that aims to find out the best feature subsets out of numerous different sets, considering multiple objectives. The Stacked Enable Hybrid Model (SEHM) plays the role of classifier and integrates with a multi-objective feature selection method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed an HF dataset from the Faisalabad Institute of Cardiology (FIOC) and evaluated six ML models, including SEHM with and without NSGA-II for experimental purposes. The Pareto front (PF) demonstrates that our introduced MO-SEHM surpasses the other models, obtaining 94.87% accuracy with the nine relevant features. Finally, we have applied Local Interpretable Model-agnostic Explanations (LIME) with MO-SEHM to explain the reasons for individual outcomes, which makes our model transparent to the patients and stakeholders.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2682"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0