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Chen Y, Lin F, Wang K, Chen F, Wang R, Lai M, Chen C, Wang R. Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning. Front Neurol 2024; 15:1255780. [PMID: 38919973 PMCID: PMC11197993 DOI: 10.3389/fneur.2024.1255780] [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] [Received: 07/09/2023] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Background The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. Methods The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. Results The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Conclusion Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.
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Affiliation(s)
- Yan Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Fabin Lin
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Kaifeng Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Chen
- Fujian Medical University, Fuzhou, Fujian, China
| | - Ruxian Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Minyun Lai
- Fujian Medical University, Fuzhou, Fujian, China
| | - Chunmei Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Rui Wang
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
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Nayak TK, Annavarappu CSR, Nayak SR, Gedefaw BM. DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising. BMC Med Imaging 2023; 23:150. [PMID: 37814250 PMCID: PMC10561479 DOI: 10.1186/s12880-023-01108-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/24/2023] [Indexed: 10/11/2023] Open
Abstract
Medical images such as CT and X-ray have been widely used for the detection of several chest infections and lung diseases. However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The presence of such noise significantly deteriorates the quality of the images and significantly affects the diagnosis performance. Hence, the design of an effective de-noising technique is highly essential to remove the noise from chest CT and X-ray images prior to further processing. Deep learning methods, mainly, CNN have shown tremendous progress on de-noising tasks. However, existing CNN based models estimate the noise from the final layers, which may not carry adequate details of the image. To tackle this issue, in this paper a deep multi-level semantic fusion network is proposed, called DMF-Net for the removal of noise from chest CT and X-ray images. The DMF-Net mainly comprises of a dilated convolutional feature extraction block, a cascaded feature learning block (CFLB) and a noise fusion block (NFB) followed by a prominent feature extraction block. The CFLB cascades the features from different levels (convolutional layers) which are later fed to NFB to attain correct noise prediction. Finally, the Prominent Feature Extraction Block(PFEB) produces the clean image. To validate the proposed de-noising technique, a separate and a mixed dataset containing high-resolution CT and X-ray images with specific and blind noise are used. Experimental results indicate the effectiveness of the DMF-Net compared to other state-of-the-art methods in the context of peak signal-to-noise ratio (PSNR) and structural similarity measurement (SSIM) while drastically cutting down on the processing power needed.
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Affiliation(s)
- Tapan Kumar Nayak
- Department of CSE, IIT(ISM) Dhanbad, Sardar Patel Nagar, Dhanbad, 826004, Jharkhand, India
| | | | - Soumya Ranjan Nayak
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India
| | - Berihun Molla Gedefaw
- Department of Health Informatics, Arba Minch University College of Medicine and Health Science, Arba Minch, Ethiopia.
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Towards Predicting Architectural Design Patterns: A Machine Learning Approach. COMPUTERS 2022. [DOI: 10.3390/computers11100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Software architecture plays an important role in software development, especially in software quality and maintenance. Understanding the impact of certain architectural patterns on software quality and verification of software requirements has become increasingly difficult with the increasing complexity of codebases in recent years. Researchers over the years have proposed automated approaches based on machine learning. However, there is a lack of benchmark datasets and more accurate machine learning (ML) approaches. This paper presents an ML-based approach for software architecture detection, namely, MVP (Model–View–Presenter) and MVVM (Model–View–ViewModel). Firstly, we present a labeled dataset that consists of 5973 data points retrieved from GitHub. Nine ML methods are applied for detection of software architecture from source code metrics. Using precision, recall, accuracy, and F1 score, the outstanding ML model performance is 83%, 83%, 83%, and 83%, respectively. The ML model’s performance is validated using k-fold validation (k = 5). Our approach outperforms when compared with the state-of-the-art.
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Tarakeswara Rao B, Lakshmana Kumar VN, Padmapriya D, Pant K, B T, Alonazi WB, Almutairi KMA, D.Raj, Ramesh Shahabadkar. Deep Neural Networks for Optimal Selection of Features Related to Flu. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:7639875. [PMID: 35873626 PMCID: PMC9303164 DOI: 10.1155/2022/7639875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/28/2022] [Indexed: 11/20/2022]
Abstract
In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.
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Affiliation(s)
- B. Tarakeswara Rao
- Department of Computer Science & Engineering, Kallam Haranadhareddy Institute of Technology, Dasaripalem, Andhra Pradesh 522019, India
| | - V. N. Lakshmana Kumar
- Department of Electronics and Communication Engineering, M.V.G.R.College of Engineering (Autonomous), Vizianagaram, Andhra Pradesh 535005, India
| | - D. Padmapriya
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India
| | - Kumud Pant
- Department of Biotechnology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002, India
| | - Tejaswini B
- Department of Information Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka 560049, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P.O. Box. 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box. 10219, Riyadh 11433, Saudi Arabia
| | - D.Raj
- Zoonosis Research Center, School of Medicine, Wonkwang University, Iksan, Republic of Korea
| | - Ramesh Shahabadkar
- Department of Electrical and Computer Engineering, Ambo University, Woliso Campus, Waliso, Ethiopia
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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