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Mustaqeem M, Mustajab S, Alam M, Jeribi F, Alam S, Shuaib M. A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI. PLoS One 2024; 19:e0307112. [PMID: 38990978 PMCID: PMC11239108 DOI: 10.1371/journal.pone.0307112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 06/30/2024] [Indexed: 07/13/2024] Open
Abstract
Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC.
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Affiliation(s)
- Mohd Mustaqeem
- Department of Computer Science, Aligarh Muslim University, Aligarh, India
| | - Suhel Mustajab
- Department of Computer Science, Aligarh Muslim University, Aligarh, India
| | - Mahfooz Alam
- Department of Computer Science, Aligarh Muslim University, Aligarh, India
| | - Fathe Jeribi
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Shadab Alam
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Mohammed Shuaib
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
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Pillay AB, Pathmanathan D, Dabo-Niang S, Abu A, Omar H. Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews. Sci Rep 2024; 14:15579. [PMID: 38971911 PMCID: PMC11227550 DOI: 10.1038/s41598-024-66246-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 06/29/2024] [Indexed: 07/08/2024] Open
Abstract
This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.
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Affiliation(s)
- Aneesha Balachandran Pillay
- Faculty of Science, Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
| | - Dharini Pathmanathan
- Faculty of Science, Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.
| | - Sophie Dabo-Niang
- Laboratoire Paul Painlevé CNRS 8524, INRIA-MODAL, Université de Lille, Villeneuve d'Ascq, France
| | - Arpah Abu
- Faculty of Science, Institute of Biological Sciences, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
| | - Hasmahzaiti Omar
- Faculty of Science, Institute of Biological Sciences, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
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Jiang M, Pan CQ, Li J, Xu LG, Li CL. Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury. Ren Fail 2023; 45:2151468. [PMID: 36645039 PMCID: PMC9848233 DOI: 10.1080/0886022x.2022.2151468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Although current guidelines didn't support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate between furosemide-responsive (FR) and furosemide-unresponsive (FU) oliguric AKI. METHODS From Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD), oliguric AKI patients with urine output (UO) < 0.5 ml/kg/h for the first 6 h after ICU admission and furosemide infusion ≥ 40 mg in the following 6 h were retrospectively selected. The MIMIC-IV cohort was used in training a XGBoost model to predict UO > 0.65 ml/kg/h during 6-24 h succeeding the initial 6 h for assessing oliguria, and it was validated in the eICU-CRD cohort. We compared the predictive performance of the XGBoost model with the traditional logistic regression and other ML models. RESULTS 6897 patients were included in the MIMIC-IV training cohort, with 2235 patients in the eICU-CRD validation cohort. The XGBoost model showed an AUC of 0.97 (95% CI: 0.96-0.98) for differentiating FR and FU oliguric AKI. It outperformed the logistic regression and other ML models in correctly predicting furosemide diuretic response, achieved 92.43% sensitivity (95% CI: 90.88-93.73%) and 95.12% specificity (95% CI: 93.51-96.3%). CONCLUSION A boosted ensemble algorithm can be used to accurately differentiate between patients who would and would not respond to furosemide in oliguric AKI. By making the model explainable, clinicians would be able to better understand the reasoning behind the prediction outcome and make individualized treatment.
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Affiliation(s)
- Meng Jiang
- Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,CONTACT Meng Jiang Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003Zhejiang Province, China
| | - Chun-qiu Pan
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China,Chun-qiu Pan Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, 510515Guangzhou, China
| | - Jian Li
- Department of Traumatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li-gang Xu
- Department of Critical Care Medicine, Wuhan Central Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chang-li Li
- Department of FSTC Clinic of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Chang-li Li Department of FSTC Clinic of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003Zhejiang Province, China
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Defect classification of glass substrate using deep neuro-fuzzy network with optimal parameter combination. GRANULAR COMPUTING 2022. [DOI: 10.1007/s41066-022-00356-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071398] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, this paper proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of COVID-19 disease. The study utilized chest X-ray images to segregate COVID-19 positive cases among healthy individuals by exploiting several combinational structures of image filtering, feature-extraction techniques, and machine learning algorithms. It analyzed the effects of three noise removal filters and two feature-extraction techniques on performance of several machine learning and deep-learning-based classifiers. The proposed schemes first remove unnecessary noise using a conservative smoothing filter, Crimmins speckle removal, and Gaussian filter. It then employs linear discriminant analysis (LDA) as linear method and principal component analysis (PCA) as non-linear feature-extraction technique to extract highly discriminant feature sets. Finally, it uses these feature sets to train various classification models, including convolutional neural network (CNN), support vector machine (SVM), and logistic regression (LG). Evidently, the proposed conservative smoothing filter with single peak to maintain symmetry in horizontal and vertical directions for enhancement of image, along with LDA and SVM, secured an overall classification accuracy of 99.93%. Experimental results show that, besides achieving high accuracies, the incorporation of feature-extraction techniques significantly reduces the computational time of the proposed model.
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Kumar V, Biswas S, Rajput DS, Patel H, Tiwari B. PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9107430. [PMID: 35800685 PMCID: PMC9253873 DOI: 10.1155/2022/9107430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/29/2022] [Indexed: 11/24/2022]
Abstract
Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.
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Affiliation(s)
- Vinod Kumar
- Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
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An Adaptive Gaussian Kernel for Support Vector Machine. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06654-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Fang Y, Wang H, Feng M, Zhang W, Cao L, Ding C, Chen H, Wei L, Mu S, Pei Z, Li J, Zhang H, Wang R, Wang S. Machine-Learning Prediction of Postoperative Pituitary Hormonal Outcomes in Nonfunctioning Pituitary Adenomas: A Multicenter Study. Front Endocrinol (Lausanne) 2021; 12:748725. [PMID: 34690934 PMCID: PMC8529112 DOI: 10.3389/fendo.2021.748725] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/15/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE No accurate predictive models were identified for hormonal prognosis in non-functioning pituitary adenoma (NFPA). This study aimed to develop machine learning (ML) models to facilitate the prognostic assessment of pituitary hormonal outcomes after surgery. METHODS A total of 215 male patients with NFPA, who underwent surgery in four medical centers from 2015 to 2021, were retrospectively reviewed. The data were pooled after heterogeneity assessment, and they were randomly divided into training and testing sets (172:43). Six ML models and logistic regression models were developed using six anterior pituitary hormones. RESULTS Only thyroid-stimulating hormone (p < 0.001), follicle-stimulating hormone (p < 0.001), and prolactin (PRL; p < 0.001) decreased significantly following surgery, whereas growth hormone (GH) (p < 0.001) increased significantly. The postoperative GH (p = 0.07) levels were slightly higher in patients with gross total resection, but the PRL (p = 0.03) level was significantly lower than that in patients with subtotal resection. The optimal model achieved area-under-the-receiver-operating-characteristic-curve values of 0.82, 0.74, and 0.85 in predicting hormonal hypofunction, new deficiency, and hormonal recovery following surgery, respectively. According to feature importance analyses, the preoperative levels of the same type and other hormones were all important in predicting postoperative individual hormonal hypofunction. CONCLUSION Fluctuation in anterior pituitary hormones varies with increases and decreases because of transsphenoidal surgery. The ML models could accurately predict postoperative pituitary outcomes based on preoperative anterior pituitary hormones in NFPA.
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Affiliation(s)
- Yi Fang
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - He Wang
- Department of Neurosurgery, The Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, The Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wentai Zhang
- Department of Neurosurgery, The Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Cao
- Department of Neurosurgery, The Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chenyu Ding
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hongjie Chen
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Liangfeng Wei
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Shuwen Mu
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Zhijie Pei
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Jun Li
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Heng Zhang
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Renzhi Wang
- Department of Neurosurgery, The Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Shousen Wang, ; Renzhi Wang,
| | - Shousen Wang
- Department of Neurosurgery, The Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
- *Correspondence: Shousen Wang, ; Renzhi Wang,
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