1
|
Talib MA, Afadar Y, Nasir Q, Nassif AB, Hijazi H, Hasasneh A. A tree-based explainable AI model for early detection of Covid-19 using physiological data. BMC Med Inform Decis Mak 2024; 24:179. [PMID: 38915001 PMCID: PMC11194929 DOI: 10.1186/s12911-024-02576-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .
Collapse
Affiliation(s)
- Manar Abu Talib
- Department of Computer Science, College of Computing and Informatics, University of Sharjah, P.O. Box 27272, Sharjah, UAE.
| | - Yaman Afadar
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Qassim Nasir
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Ali Bou Nassif
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Haytham Hijazi
- Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, P-3030-290, Portugal
- Intelligent Systems Department, Ahliya University, Bethlehem, P-150-199, Palestine
| | - Ahmad Hasasneh
- Department of Natural, Engineering and Technology Sciences, Faculty of Graduate Studies, Arab American University, P.O. Box 240, Ramallah, Palestine
| |
Collapse
|
2
|
Yakovyna V, Shakhovska N, Szpakowska A. A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction. Sci Rep 2024; 14:9782. [PMID: 38684770 PMCID: PMC11059164 DOI: 10.1038/s41598-024-60637-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/25/2024] [Indexed: 05/02/2024] Open
Abstract
Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.
Collapse
Affiliation(s)
- Vitaliy Yakovyna
- Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Ul. Oczapowskiego 2, 10-719, Olsztyn, Poland
- Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine
| | - Nataliya Shakhovska
- Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine.
- Universytet Rolniczy, 31120, Kraków, Poland.
| | - Aleksandra Szpakowska
- Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Ul. Oczapowskiego 2, 10-719, Olsztyn, Poland
| |
Collapse
|
3
|
Amgothu S, Koppu S. COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture. PLoS One 2023; 18:e0295599. [PMID: 38127990 PMCID: PMC10735048 DOI: 10.1371/journal.pone.0295599] [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: 09/15/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023] Open
Abstract
In the pandemic of COVID-19 patients approach to the hospital for prescription, yet due to extreme line up the patient gets treatment after waiting for more than one hour. Generally, wearable devices directly measure the preliminary data of the patient stored in capturing mode. In order to store the data, the hospitals require large storage devices that make the progression of data more complex. To bridge this gap, a potent scheme is established for COVID-19 prediction based fog-cloud named Caviar Squirrel Jellyfish Search Optimization (CSJSO). Here, CSJSO is the amalgamation of CAViar Squirrel Search Algorithm (CSSA) and Jellyfish Search Optimization (JSO), where CSSA is blended by the Conditional Autoregressive Value-at-Risk (CAViar) and Squirrel Search Algorithm (SSA). This architecture comprises the healthcare IoT sensor layer, fog layer and cloud layer. In the healthcare IoT sensor layer, the routing process with the collection of patient health condition data is carried out. On the other hand, in the fog layer COVID-19 detection is performed by employing a Deep Neuro Fuzzy Network (DNFN) trained by the proposed Remora Namib Beetle JSO (RNBJSO). Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). Finally, in the cloud layer, the detection of COVID-19 employing Deep Long Short Term Memory (Deep LSTM) trained utilizing proposed CSJSO is performed. The evaluation measures utilized for CSJSO_Deep LSTM in database-1, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) observed 0.062 and 0.252 in confirmed cases. The measures employed in database-2 are accuracy, sensitivity and specificity achieved 0.925, 0.928 and 0.925 in K-set.
Collapse
Affiliation(s)
- Shanthi Amgothu
- School of Computer Science Engineering and Information Systems, Vellore, India
| | - Srinivas Koppu
- School of Computer Science Engineering and Information Systems, Vellore, India
| |
Collapse
|
4
|
Nambiar A, S H, S S. Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data. Front Artif Intell 2023; 6:1272506. [PMID: 38111787 PMCID: PMC10726049 DOI: 10.3389/frai.2023.1272506] [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: 09/05/2023] [Accepted: 11/07/2023] [Indexed: 12/20/2023] Open
Abstract
Introduction The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in the wild. Methods In this regard, this study explores the use of model-agnostic XAI models, such as SHapley Additive exPlanations values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), for COVID-19 symptom analysis in Indian patients toward a COVID severity prediction task. Various machine learning models such as Decision Tree Classifier, XGBoost Classifier, and Neural Network Classifier are leveraged to develop Machine Learning models. Results and discussion The proposed XAI tools are found to augment the high performance of AI systems with human interpretable evidence and reasoning, as shown through the interpretation of various explainability plots. Our comparative analysis illustrates the significance of XAI tools and their impact within a healthcare context. The study suggests that SHAP and LIME analysis are promising methods for incorporating explainability in model development and can lead to better and more trustworthy ML models in the future.
Collapse
Affiliation(s)
- Athira Nambiar
- Department of Computational Intelligence, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | | | | |
Collapse
|
5
|
Chadaga K, Prabhu S, Bhat V, Sampathila N, Umakanth S, Upadya P S. COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador. SLAS Technol 2023; 28:393-410. [PMID: 37689365 DOI: 10.1016/j.slast.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/16/2023] [Accepted: 09/06/2023] [Indexed: 09/11/2023]
Abstract
The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.
Collapse
Affiliation(s)
- Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - Vivekananda Bhat
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - Shashikiran Umakanth
- Department of Medicine, Dr. TMA Hospital, Manipal Academy of Higher Education, Manipal, India
| | - Sudhakara Upadya P
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| |
Collapse
|
6
|
Moadab A, Kordi G, Paydar MM, Divsalar A, Hajiaghaei-Keshteli M. Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era. EXPERT SYSTEMS WITH APPLICATIONS 2023; 227:120334. [PMID: 37192999 PMCID: PMC10162855 DOI: 10.1016/j.eswa.2023.120334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/09/2023] [Accepted: 04/29/2023] [Indexed: 05/18/2023]
Abstract
Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.
Collapse
Affiliation(s)
- Amirhossein Moadab
- Department of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA, USA
| | - Ghazale Kordi
- Department of Economics and Management, University of Helsinki, Helsinki, Finland
| | - Mohammad Mahdi Paydar
- Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Ali Divsalar
- Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Mostafa Hajiaghaei-Keshteli
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
| |
Collapse
|