Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

Lalmuanawma, 2020, Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review, Chaos, Solit Fractals, 139, 10.1016/j.chaos.2020.110059 Heidari, 2022, Machine learning applications for COVID-19 outbreak management, Neural Comput Appl, 34, 15313, 10.1007/s00521-022-07424-w Bhardwaj, 2021, A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality, Int J Imag Syst Technol, 31, 1775, 10.1002/ima.22627 Tandan, 2021, Discovering symptom patterns of COVID-19 patients using association rule mining, Comput Biol Med, 131, 10.1016/j.compbiomed.2021.104249 Qorib, 2023, Covid-19 vaccine hesitancy: text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset, Expert Syst Appl, 212, 10.1016/j.eswa.2022.118715 Lau, 2023, Real-world COVID-19 vaccine effectiveness against the Omicron BA. 2 variant in a SARS-CoV-2 infection-naive population, Nat Med, 29, 348, 10.1038/s41591-023-02219-5 Stati, 2023, Concern about the effectiveness of mRNA vaccination technology and its long-term safety: potential interference on miRNA machinery, Int J Mol Sci, 24, 1404, 10.3390/ijms24021404 Al-Waisy, 2023, COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images, Soft Comput, 27, 2657, 10.1007/s00500-020-05424-3 Dehghani, 2023, ParsBERT topic modeling of Persian scientific articles about COVID-19, Inform Med Unlocked, 36, 10.1016/j.imu.2022.101144 Abbasi, 2021, Hemoperfusion in patients with severe COVID-19 respiratory failure, lifesaving or not?, J Res Med Sci: off j Isfahan Univ Med Sci, 26 Zhan, 2021, An investigation of testing capacity for evaluating and modeling the spread of coronavirus disease, Inf Sci, 561, 211, 10.1016/j.ins.2021.01.084 Alghamdi, 2021, Deep learning approaches for detecting COVID-19 from chest X-ray images: a survey, IEEE Access, 9, 20235, 10.1109/ACCESS.2021.3054484 Dargan, 2020, A survey of deep learning and its applications: a new paradigm to machine learning, Arch Comput Methods Eng, 27, 1071, 10.1007/s11831-019-09344-w Shishvan, 2018, Machine intelligence in healthcare and medical cyber physical systems: a survey, IEEE Access, 6, 46419, 10.1109/ACCESS.2018.2866049 C. Chen, "Ascent of machine learning in medicine.". Alballa, 2021, Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: a review, Inform Med Unlocked, 24, 10.1016/j.imu.2021.100564 Domadiya, 2021, Privacy preserving association rule mining on distributed healthcare data: covid-19 and breast cancer case study, SN Comp Sci, 2, 418, 10.1007/s42979-021-00801-7 Borah, 2018, Identifying risk factors for adverse diseases using dynamic rare association rule mining, Expert Syst Appl, 113, 233, 10.1016/j.eswa.2018.07.010 Karabatak, 2009, An expert system for detection of breast cancer based on association rules and neural network, Expert Syst Appl, 36, 3465, 10.1016/j.eswa.2008.02.064 Garg, 2021, Role of machine learning in medical research: a survey, Comp sci rev, 40 Shen, 2017, Deep learning in medical image analysis, Annu Rev Biomed Eng, 19, 221, 10.1146/annurev-bioeng-071516-044442 Strzelecki, 2022, vol. 12, 2022 Mi, 2021, Permutation-based identification of important biomarkers for complex diseases via machine learning models, Nat Commun, 12, 3008, 10.1038/s41467-021-22756-2 Xie, 2021, Early lung cancer diagnostic biomarker discovery by machine learning methods, Transl oncol, 14, 10.1016/j.tranon.2020.100907 Chang, 2022, Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning, Comput Struct Biotechnol J, 20, 4600, 10.1016/j.csbj.2022.08.029 Carracedo-Reboredo, 2021, A review on machine learning approaches and trends in drug discovery, Comput Struct Biotechnol J, 19, 4538, 10.1016/j.csbj.2021.08.011 Popova, 2018, Deep reinforcement learning for de novo drug design, Sci Adv, 4, eaap7885, 10.1126/sciadv.aap7885 Jiang, 2023, DECAF: an interpretable deep cascading framework for ICU mortality prediction, Artif Intell Med, 138, 10.1016/j.artmed.2022.102437 Nistal-Nuño, 2022, Developing machine learning models for prediction of mortality in the medical intensive care unit, Comput Methods Progr Biomed, 216, 10.1016/j.cmpb.2022.106663 Khedr, 2021, An efficient association rule mining from distributed medical databases for predicting heart diseases, IEEE Access, 9, 15320, 10.1109/ACCESS.2021.3052799 a. F. M. Inamdar, 2022, Heart disease predictive analysis using association rule mining, 111 Fernandez-Basso, 2022, A fuzzy-based medical system for pattern mining in a distributed environment: application to diagnostic and co-morbidity, Appl Soft Comput, 122, 10.1016/j.asoc.2022.108870 Dabla, 2022, Target association rule mining to explore novel paediatric illness patterns in emergency settings, Scand J Clin Lab Investig, 82, 595, 10.1080/00365513.2022.2148121 Lakshmi, 2019, A novel approach for disease comorbidity prediction using weighted association rule mining, J Ambient Intell Hum Comput, 1 Mohapatra, 2019, Analysis of tuberculosis disease using association rule mining, vol. 2021, 995 Cui, 2022, An association rule mining algorithm for clinical decision support, 137 Alam, 2021, A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining, Biomed Signal Process Control, 68, 10.1016/j.bspc.2021.102726 Keyvanpour, 2022, WARM: a new breast masses classification method by weighting association rule mining, Signal, Image and Video Processing, 1 Ramezankhani, 2015, An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database, Int J Endocrinol Metabol, 13, 10.5812/ijem.25389 Gakii, 2021, Identification of cancer related genes using feature selection and association rule mining, Inform Med Unlocked, 24, 10.1016/j.imu.2021.100595 Veroneze, 2020, Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases, PLoS One, 15, 10.1371/journal.pone.0240269 Yamamoto, 2023, Early detection of adverse drug reaction signals by association rule mining using large-scale administrative claims data, Drug Saf, 46, 371, 10.1007/s40264-023-01278-4 Jain, 2020, A deep learning approach to detect Covid-19 coronavirus with X-Ray images, Biocybern Biomed Eng, 40, 1391, 10.1016/j.bbe.2020.08.008 Ismael, 2021, Deep learning approaches for COVID-19 detection based on chest X-ray images, Expert Syst Appl, 164, 10.1016/j.eswa.2020.114054 Shahin, 2022, Machine learning approach for autonomous detection and classification of COVID-19 virus, Comput Electr Eng, 101, 10.1016/j.compeleceng.2022.108055 Moulaei, 2022, Comparing machine learning algorithms for predicting COVID-19 mortality, BMC Med Inf Decis Making, 22, 1 Nawaz, 2021, Using artificial intelligence techniques for COVID-19 genome analysis, Appl Intell, 51, 3086, 10.1007/s10489-021-02193-w Nawaz, 2023, Using alignment-free and pattern mining methods for SARS-CoV-2 genome analysis, Appl Intell, 1 Singh, 2023, Investigating new patterns in symptoms of COVID-19 patients by Association Rule Mining (ARM), J Mobile Multim, 19, 1 Matharaarachchi, 2022, Discovering long covid symptom patterns: association rule mining and sentiment analysis in social media tweets, JMIR Formative Res., 6, 10.2196/37984 Agrawal, 1994, Fast algorithms for mining association rules, vol. 1215, 487 Cheng, 2017, Healthcare data mining, association rule mining, and applications, Health Inf Data Anal: Methods and Examples, 201 Ordonez, 2006, Constraining and summarizing association rules in medical data, Knowl Inf Syst, 9, 1, 10.1007/s10115-005-0226-5 Agrawal, 1993, Mining association rules between sets of items in large databases, 207 Rai, 2023, Association rule mining for prediction of COVID-19, Decision Making: Appl Manag Eng, 6, 365 Ilbeigipour, 2022, Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction, Inform Med Unlocked, 30, 10.1016/j.imu.2022.100933 Luna, 2018, Optimization of quality measures in association rule mining: an empirical study, Int J Comput Intell Syst, 12, 59, 10.2991/ijcis.2018.25905182 Gulzar, 2023, An efficient healthcare data mining approach using Apriori algorithm: a case study of eye disorders in young adults, Information, 14, 203, 10.3390/info14040203 Li, 2017, Mining association rules between stroke risk factors based on the Apriori algorithm, Technol Health Care, 25, 197, 10.3233/THC-171322 Nahar, 2013, Association rule mining to detect factors which contribute to heart disease in males and females, Expert Syst Appl, 40, 1086, 10.1016/j.eswa.2012.08.028 Puram, 2023, vol. 6, 164 Cummings, 2020, Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study, Lancet, 395, 1763, 10.1016/S0140-6736(20)31189-2 Talbot, 2021, Coronavirus disease 2019 (COVID-19) versus influenza in hospitalized adult patients in the United States: differences in demographic and severity indicators, Clin Infect Dis, 73, 2240, 10.1093/cid/ciab123 Wang, 2020, Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China, Clin Infect Dis, 71, 769, 10.1093/cid/ciaa272 Peravali, 2021, A systematic review and meta-analysis of clinical characteristics and outcomes in patients with lung cancer with coronavirus disease 2019, JTO clin res rep, 2 Czubak, 2021, Comparison of the clinical differences between COVID-19, SARS, influenza, and the common cold: a systematic literature review, Adv Clin Exp Med, 30, 109, 10.17219/acem/129573 Keshavarzi, 2021, Seizure is a rare presenting manifestation of COVID-19, Seizure, 86, 16, 10.1016/j.seizure.2021.01.009 Loffredo, 2020, Conjunctivitis and COVID‐19: a meta‐analysis, J Med Virol, 92, 1413, 10.1002/jmv.25938 Sindhuja, 2020, Clinical profile and prevalence of conjunctivitis in mild COVID-19 patients in a tertiary care COVID-19 hospital: a retrospective cross-sectional study, Indian J Ophthalmol, 68, 1546, 10.4103/ijo.IJO_1319_20 Layikh, 2021, Conjunctivitis and other ocular findings in patients with COVID-19 infection, Ann Saudi Med, 41, 280, 10.5144/0256-4947.2021.280 Gupta, 2021, A systematic review and meta-analysis of diabetes associated mortality in patients with COVID-19, Int J Endocrinol Metabol, 19, 10.5812/ijem.113220 Grant, 2020, The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries, PLoS One, 15, 10.1371/journal.pone.0234765 Giri, 2021, Comparison of clinical manifestations, pre-existing comorbidities, complications and treatment modalities in severe and non-severe COVID-19 patients: a systemic review and meta-analysis, Sci Prog, 104, 10.1177/00368504211000906 Alimohamadi, 2020, Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis, J prev med hyg, 61, E304 Otuonye, 2021, Clinical and demographic characteristics of COVID-19 patients in Lagos, Nigeria: a descriptive study, J Natl Med Assoc, 113, 301 Schettino, 2021, Clinical characteristics of COVID-19 patients with gastrointestinal symptoms in Northern Italy: a single-center cohort study, Off j Am Coll Gastroenterol, 116, 306, 10.14309/ajg.0000000000000965 Branson, 2021, The US strategic national stockpile ventilators in coronavirus disease 2019: a comparison of functionality and analysis regarding the emergency purchase of 200,000 devices, Chest, 159, 634, 10.1016/j.chest.2020.09.085 Han, 2000, Mining frequent patterns by pattern-growth: methodology and implications, ACM SIGKDD explor newsl, 2, 14, 10.1145/380995.381002 Zaki, 1997, New algorithms for fast discovery of association rules, KDD, 97, 283