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Cohesive prediction model for analyzing the recurrent attributes of infectious cryptosporidiosis disease

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Abstract

The growth of computing techniques has reduced the complications in handling medical and healthcare data for optimal processing. We modeled the infectious disease cryptosporidiosis, which is a parasitic disease, to inform the management of its spread. We used a cohesive prediction model (CPM) for analyzing the nature of the cryptosporidiosis disease through a series of observed data. In this prediction model, the disease's characteristics and its attributes were used to predict its nature over a specific period. The prediction model relies on the attributes of cryptosporidiosis diseases and its adverse effects from its time of emergence. The attributes and recurrent cryptosporidiosis disease data are analyzed using Naive Bayes classification learning. The time recurrence data of cryptosporidiosis disease is predictively handled using prior classifications and validations of the analysis's new attributes. Using this information, the predictive model maps all possible attributes of the cryptosporidiosis disease in time recurrence. The experimental results showed that the suggested system was validated by datasets from the European Centre for Disease Prevention and Control, with an accuracy ratio of 95.55%, the sensitivity of 94.10%, and specificity of 94.89% in predicting and modeling the attributes of cryptosporidiosis disease.

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References

  • Alsiddiky A, Fouad H, Soliman A, Altinawi A, Mahmoud N (2020) Vertebral tumor detection and segmentation using analytical transform assisted statistical characteristic decomposition model. IEEE Access 8:145278–145289

    Article  Google Scholar 

  • Al-Toma A, Volta U, Auricchio R, Castillejo G, Sanders DS, Cellier C, Mulder CJ, Lundin KE (2019) European Society for the Study of Coeliac Disease (ESsCD) guideline for coeliac disease and other gluten-related disorders. United Eur Gastroenterol J 7(5):583–613

    Article  Google Scholar 

  • Berke O, Trotz-Williams L, De Montigny S (2020) Good times bad times: Automated forecasting of seasonal Cryptosporidiosis in Ontario using machine learning. Canada Commun Dis Rep 46(6):192–197

    Article  Google Scholar 

  • Bhandari D, Bi P, Sherchand JB, Dhimal M, Hanson-Easey S (2020) Climate change and infectious disease research in Nepal: are the available prerequisites supportive enough to researchers? Acta Tropica 1(204):105337

    Article  Google Scholar 

  • Bonyah E, Twagirumukiza G, Gambrah PP (2019) Mathematical analysis of diarrhoea model with saturated incidence rate. Open J Math Sci 2019(3):29–39. https://doi.org/10.30538/oms2019.0046

    Article  Google Scholar 

  • Bouchard C, Dibernardo A, Koffi J, Wood H, Leighton PA, Lindsay LR (2019) Climate change and infectious diseases: the challenges: N increased risk of tick-borne diseases with climate and environmental changes. Canada Commun Dis Rep 45(4):83

    Article  Google Scholar 

  • Brainard J, Hooper L, McFarlane S, Hammer CC, Hunter PR, Tyler K (2020) Systematic review of modifiable risk factors shows little evidential support for most current practices in Cryptosporidium management in bovine calves. Parasitology Research 119:3571–3584

    Article  Google Scholar 

  • Breternitz BS, da Veiga DPB, Razzolini MTP, Nardocci AC (2020) Land use associated with Cryptosporidium sp and Giardia sp in surface water supply in the state of São Paulo Brazil. Environ Pollut 266:115143

    Article  Google Scholar 

  • Carvalho I, Silva N, Carrola J, Silva V, Currie C, Igrejas G, Poeta P (2019) Antibiotic resistance: immunity-acquired resistance: evolution of antimicrobial resistance among extended-spectrum β-lactamases and carbapenemases in Klebsiella pneumoniae and Escherichia coli. Antibiotic Drug Resist 9:239–59

    Article  Google Scholar 

  • Chaturvedi O, Jeffrey M, Lungu E, Masupe S (2017) Epidemic model formulation and analysis for diarrheal infections caused by salmonella. Simulation 93(7):543–552

    Article  Google Scholar 

  • Cong L, Bremer P, Mirosa M (2019) Chinese consumers’ perceptions of functional foods: a netnography study of foods that help the immune system recover from air pollution. J Food Prod Market 25(6):628–46

    Article  Google Scholar 

  • Elshahawy I, AbouElenien F (2019) Seroprevalence of Cryptosporidium and risks of Cryptosporidiosis in residents of Sothern Egypt: a cross-sectional study. Asian Pacific J Trop Med 12(5):232

    Article  Google Scholar 

  • Fallahi S, Moosavi SF, Karimi A, Chegeni AS, Saki M, Namdari P, Almasian M (2018) An advanced uracil DNA glycosylase-supplemented loop-mediated isothermal amplification (UDG-LAMP) technique used in the sensitive and specific detection of Cryptosporidium parvum, Cryptosporidium hominis, and Cryptosporidium meleagridis in AIDS patients. Diagn Microbiol Infect Dis 91(1):6–12

    Article  Google Scholar 

  • Fouad H, Hassanein A, Soliman A, Al-Feel H (2020) Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction. Measurement 159:107757

    Article  Google Scholar 

  • Fouad H, Soliman A, Hassanein A, Al-Feel H (2020) Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04935-2

    Article  Google Scholar 

  • Hamdy D, El-Badry A, Abd El Wahab W (2019) Assessment of giardia and cryptosporidium assemblages/species and their viability in potable tap water in Beni-Suef, Egypt using nested PCR/RFLP and staining. Iran J Parasitol 14(3):368

    Google Scholar 

  • Iaccarino M (2019) Water, population growth and contagious diseases. Water 11(2):386

    Article  Google Scholar 

  • Ichikawa-Seki M, Motooka D, Kinami A, Murakoshi F, Takahashi Y, Aita J, Hayashi K, Tashibu A, Nakamura S, Iida T, Horii T (2019) Specific increase of fusobacterium in the faecal microbiota of neonatal calves infected with Cryptosporidium parvum. Sci Rep 9(1):1–8

    Article  Google Scholar 

  • Innes EA, Chalmers RM, Wells B, Pawlowic MC (2020) A one health approach to tackle cryptosporidiosis. Trends Parasitol 36(3):290–303

    Article  Google Scholar 

  • Kortbeek RW, van der Gragt M, Bleeker PM (2019) Endogenous plant metabolites against insects. Eur J Plant Pathol 154(1):67–90

    Article  Google Scholar 

  • Lal A, Fearnley E, Wilford E (2019) Local weather, flooding history and childhood diarrhoea caused by the parasite Cryptosporidium spp.: a systematic review and meta-analysis. Sci Total Environ 674:300–6

    Article  Google Scholar 

  • Luka G, Samiei E, Dehghani S, Johnson T, Najjaran H, Hoorfar M (2019) Label-free capacitive biosensor for detection of cryptosporidium. Sensors 19(2):258

    Article  Google Scholar 

  • Mahmoud N, Fouad H, Alsadon O, Soliman A (2020) Detecting dental problem related brain disease using intelligent bacterial optimized associative deep neural network. Cluster Comput 23(3):1647–1657

    Article  Google Scholar 

  • McCluskey SM, Siedner MJ, Marconi VC (2019) Management of virologic failure and HIV drug resistance. Infect Dis Clin 33(3):707–42

    Article  Google Scholar 

  • Mourad N, Michel RP, Marcus VA (2019) Pathology of gastrointestinal and liver complications of hematopoietic stem cell transplantation. Arch Pathol Laborat Med 143(9):1131–43

    Article  Google Scholar 

  • Pergam SA, Limaye AP (2019) AST infectious diseases community of practice varicella zoster virus in solid organ transplantation: guidelines from the American Society of Transplantation infectious diseases community of practice. Clin Transpl 33(9):13622

    Article  Google Scholar 

  • Perlmutter JI, Bordenstein SR (2020) Microorganisms in the reproductive tissues of arthropods. Nat Rev Microbiol 6:1–5

    Google Scholar 

  • Sannella AR, Suputtamongkol Y, Wongsawat E, Cacciò SM (2019) A retrospective molecular study of Cryptosporidium species and genotypes in HIV-infected patients from Thailand. Parasit Vectors 12(1):1–6

    Article  Google Scholar 

  • Sazama EJ, Ouellette SP, Wesner JS (2019) Bacterial endosymbionts are common among, but not necessarily within, insect species. Environ Entomol 48(1):127–33

    Article  Google Scholar 

  • Shakeel PM, Baskar S, Dhulipala VS, Jaber MM (2018) Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inform Sci Syst 6(1):16. https://doi.org/10.1007/s13755-018-0054-0

    Article  Google Scholar 

  • Tsukano K, Lakritz J, Suzuki K (2020) Plasma amino acid status is useful for understanding intestinal mucosal damage in calves with cryptosporidiosis. Amino Acids. https://doi.org/10.1007/s00726-020-02904-6

    Article  Google Scholar 

  • Xiao S, Hu S, Zhang Y, Zhao X, Pan W (2018) Influence of sewage treatment plant effluent discharge into multipurpose river on its water quality: a quantitative health risk assessment of Cryptosporidium and Giardia. Environ Pollut 233:797–805

    Article  Google Scholar 

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Acknowledgements

The authors would like to extend their gratitude to King Saud University (Riyadh, Saudi Arabia) for funding this research through Researchers Supporting Project number (RSP-2020/241).

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Authors and Affiliations

  1. Department of Biochemistry, College of Science, King Saud University, PO Box 22452, Riyadh, 11451, Saudi Arabia

    Mohamed H. Mahmoud & Salman Alamery

  2. Biomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia

    Amir Altinawi

  3. Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt

    Hassan Fouad

  4. Biomedical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Nourelhoda M. Mahmoud

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  1. Mohamed H. Mahmoud
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  2. Salman Alamery
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  3. Amir Altinawi
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  4. Hassan Fouad
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  5. Nourelhoda M. Mahmoud
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Correspondence to Mohamed H. Mahmoud.

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Mahmoud, M.H., Alamery, S., Altinawi, A. et al. Cohesive prediction model for analyzing the recurrent attributes of infectious cryptosporidiosis disease. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-020-02861-8

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