A Novel Approach to Analysis Consequence of Climate changes on Erythemato-squamous Diseases using Machine Learning Algorithms

A Novel Approach to Analysis Consequence of Climate changes on Erythemato-squamous Diseases using Machine Learning Algorithms
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Shaik Abdul Khalandar Basha, P. M. Durai Raj Vincent, K. Srividya, Pusarla Samyuktha, Jessu Madhumathi
DOI : 10.14445/22315381/IJETT-V70I11P202

How to Cite?

Shaik Abdul Khalandar Basha, P. M. Durai Raj Vincent, K. Srividya, Pusarla Samyuktha, Jessu Madhumathi, "A Novel Approach to Analysis Consequence of Climate changes on Erythemato-squamous Diseases using Machine Learning Algorithms," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 10-18, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P202

Abstract
Erythemato-squamous Disease is considered an arboviral disease that falls under the subset of skin disease. Nearly 15% of death caused due to skin diseases are getting registered in the WHO IIPC survey, which can be considered a major threat to public health. In our retrospective study, we investigated ESD incidence with 34 attributes and attempted to determine and diagnose the effects of climate attributes on ESD. In the paper, weather data from Chicago and turkey are taken. Among that, 22 attributes from the first data set and 34 attributes from the second dataset are considered, with this total no of 49 unique attributes taken into consideration for the analysis. In this work, the Weather-Method-Diseases framework by using Machine Learning Algorithms. Pearson’s correlation coefficient factor is used to measure the strength of a relationship that exists among 49 attributes for the analysis. The finding in our research is presented, and the result analysis shows the superiority of the proposed approach; these 23attributes are highly correlated. These attributes can be considered to be the most deciding factor for predicting six different types of skin diseases in the future using this proposed framework.

Keywords
Erythemato-squamous Diseases (ESD), W-M-D framework, Machine Learning Algorithms, Pearson’s correlation coefficient.

Reference
[1] O. Babalola, A. Razzaque, and D. Bishai, “Temperature Extremes and Infant Mortality in Bangladesh : Hotter Months, Lower Mortality,” PLOS ONE, vol. 14, no. 5, pp. 1–9, 2018. Crossref, https://doi.org/10.1371/journal.pone.0189252
[2] A. A. Jacobsen, A. Galvan, C. C. Lachapelle, C. B. Wohl, R. S. Kirsner, and J. Strasswimmer, “Defining the Need for Skin Cancer Prevention Education in Uninsured, Minority, and Immigrant Communities,” JAMA Dermatol, vol. 152, no. 12, pp. 1342–1347, 2016. Crossref, https://doi.org/10.1001/jamadermatol.2016.3156
[3] Balmain BN, Jay O, Sabapathy S, Royston D, Stewart GM, Jayasinghe R, and Morris NR, “Altered Thermoregulatory Responses in Heart Failure Patients Exercising in the Heat,” Physiological Reports, vol. 4, no. 21, 2016. Crossref, https://doi.org/10.14814/phy2.13022.
[4] Noah Scovronick, Francesco Sera, Fiorella Acquaotta, Diego Garzena, Simona Fratianni, Caradee Y. Wright, and Antonio Gasparrini, “The Association Between Ambient Temperature and Mortality in South Africa: A Time-Series Analysis,” Environmental Research, vol. 161, pp. 229–235, 2018. Crossref, https://doi.org/10.1016/j.envres.2017.11.001
[5] Eric Lavigne, Antonio Gasparrini, Xiang Wang, Hong Chen, Abderrahmane Yagouti, Manon D Fleury and Sabit Cakmak, “Extreme Ambient Temperatures and Cardiorespiratory Emergency Room Visits: Assessing Risk by Comorbid Health Conditions in a Time Series Study,” Environmental Health: A Global Access Science Source, vol. 13, no. 1, pp. 1–8, 2014. Crossref, https://doi.org/10.1186/1476-069X-13-5
[6] Li Bai, Gangqiang Ding, Shaohua Gu, Peng Bi, Buda Su, Dahe Qin, Guozhang Xu, and Qiyong Liu, “The Effects of Summer Temperature and Heat Waves on Heat-Related Illness in a Coastal City of China, 2011 – 2013,” Environmental Research, vol. 132, pp. 212–219, 2014. Crossref, https://doi.org/10.1016/j.envres.2014.04.002
[7] C. Bertelsmeier, G. M. Luque, and F. Courchamp, “The Impact of Climate Change Changes Over Time,” Biological Conservation, vol. 167, pp. 107–115, 2013. Crossref, https://doi.org/10.1016/j.biocon.2013.07.038
[8] Z. C. Félix Garza, J. Liebmann, M. Born, P. A. J. Hilbers, and N. A. W. van Riel, “A Dynamic Model for Prediction of Psoriasis Management by Blue Light Irradiation,” Frontiers in Physiology, vol. 8, no. 1, pp. 1–17, 2017. Crossref, https://doi.org/10.3389/fphys.2017.00028
[9] K. Mohammadi, S. Shamshirband, S. Motamedi, D. Petković, R. Hashim, and M. Gocic, “Extreme Learning Machine Based Prediction of Daily Dew Point Temperature,” Computers and Electronics in Agriculture, vol. 117, pp. 214–225, 2015. Crossref, https://doi.org/10.1016/j.compag.2015.08.008Get
[10] S. Kundu, D. Khare, and A. Mondal, “Future Changes In Rainfall, Temperature and Reference Evapotranspiration in the Central India by Least Square Support Vector Machine,” Geoscience Frontiers, vol. 8, no. 3, pp. 583–596, 2017. Crossref, https://doi.org/10.1016/j.gsf.2016.06.002
[11] J. M. Lobo, “The Use of Occurrence Data to Predict the Effects of Climate Change on Insects,” Current Opinion in Insect Science, vol. 17, pp. 62–68, 2016. Crossref, https://doi.org/10.1016/j.cois.2016.07.003
[12] S. Lou, D. H. W. Li, J. C. Lam, and W. W. H. Chan, “Prediction of Diffuse Solar Irradiance using Machine Learning and Multivariable Regression,” Applied Energy, vol. 181, pp. 367–374, 2016. Crossref, https://doi.org/10.1016/j.apenergy.2016.08.093
[13] Terence Dwyer, James M Stankovich, Leigh Blizzard, Liesel M FitzGerald, Joanne L Dickinson, Anne Reilly, Jan Williamson, Rosie Ashbolt, Marianne Berwick, and Michèle M Sale, “Does the Addition of Information on Genotype Improve Prediction of the Risk of Melanoma and Nonmelanoma Skin Cancer Beyond that Obtained from Skin Phenotype?,” American Journal of Epidemiology, vol. 159, no. 9, pp. 826–833, 2004. Crossref, https://doi.org/10.1093/aje/kwh120
[14] B. H. Kaffenberger, D. Shetlar, S. A. Norton, and M. Rosenbach, “The Effect of Climate Change on Skin Disease in North America,” Journal of the American Academy of Dermatology, vol. 76, no. 1, pp. 140–147, 2017. Crossref, https://doi.org/10.1016/j.jaad.2016.08.014
[15] H. A. Güvenir and N. Emeksiz, “Expert System for the Differential Diagnosis of Erythemato-Squamous Diseases,” Expert Systems with Applications, vol. 18, no. 1, pp. 43–49, 2000. Crossref, https://doi.org/10.1016/S0957-4174(99)00049-4
[16] A. M. Hashem, T. Abujamel, R. Alhabbab, M. Almazroui, and E. I. Azhar, “Dengue Infection in Patients with Febrile Illness and its Relationship to Climate Factors: A Case Study in the City of Jeddah, Saudi Arabia, for the Period 2010–2014,” Acta Tropica, vol. 181, pp. 105–111, 2018. Crossref, https://doi.org/10.1016/j.actatropica.2018.02.014
[17] Qi Zhao, Yi Zhao, Shanshan Li, Yajuan Zhang, Qingan Wang, Huiling Zhang, Hui Qiao, Wuping Li, Rachel Huxley, Gail Williams, Yuhong Zhang, and Yuming Guo, “Impact of ambient Temperature on Clinical Visits for Cardio-Respiratory Diseases in Rural Villages in Northwest China,” Science of the Total Environment, vol. 612, pp. 379–385, 2018. Crossref, https://doi.org/10.1016/j.scitotenv.2017.08.244
[18] Gopalakrishnan.S, Dr.Ebenezer Abishek.B, Dr. A. Vijayalakshmi, Dr. V. Rajendran, "Analysis and Diagnosis Using Deep -Learning Algorithm on Erythemato-Squamous Disease," International Journal of Engineering Trends and Technology, vol. 69, no. 3, pp. 52-57, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I3P210
[19] M. Grasso, M. Manera, A. Chiabai, and A. Markandya, “The Health Effects of Climate Change: A Survey of Recent Quantitative Research,” International Journal of Environmental Research and Public Health, vol. 9, no. 5, pp. 1523–1547, 2012. Crossref, https://doi.org/10.3390/ijerph9051523
[20] S. C. Harrison and W. F. Bergfeld, “Ultraviolet light and Skin Cancer in Athletes,” Sports Health, vol. 1, no. 4, pp. 335–340, 2009. Crossref, https://doi.org/10.1177/1941738109338923
[21] E. D. Übeyli and I. Güler, “Automatic Detection of Erythemato-Squamous Diseases using Adaptive Neuro-Fuzzy Inference Systems,” Computers in Biology and Medicine, vol. 35, no. 5, pp. 421–433, 2005. Crossref, https://doi.org/10.1016/j.compbiomed.2004.03.003
[22] Sang Gyu Lee, Sung Kyeom Kim, Hee Ju Lee, Hee Su Lee, and Jin Hyoung Lee, “Impact of Moderate and Extreme Climate Change Scenarios on Growth, Morphological Features, Photosynthesis, and Fruit Production of Hot Pepper,” Ecology and Evolution, vol. 8, no. 1, pp. 197–206, 2018. Crossref, https://doi.org/10.1002/ece3.3647
[23] Brecht Devleesschauwer, Massimiliano Marvasi , Mihai C Giurcanu, George J Hochmuth, Niko Speybroeck, Arie H Havelaar, and Max Teplitski, “High Relative Humidity Pre-Harvest Reduces Post-Harvest Proliferation of Salmonella in Tomatoes,” Food Microbiology, vol. 66, pp. 55–63, 2017. Crossref, https://doi.org/10.1016/j.fm.2017.04.003
[24] L. Cecchi, G. D’Amato, and I. Annesi-Maesano, “External Exposome and Allergic Respiratory and Skin Diseases,” The Journal of Allergy and Clinical Immunology, vol. 141, no. 3, pp. 846–857, 2018. Crossref, https://doi.org/10.1016/j.jaci.2018.01.016
[25] S. He, T. Kosatsky, A. Smargiassi, and M. Bilodeau-bertrand, “Heat and Pregnancy-Related Emergencies : Risk of Placental Abruption During Hot Weather,” Environment International, vol. 111, pp. 295-300, 2018. Crossref, https://doi.org/10.1016/j.envint.2017.11.004
[26] R. Gordon, “Skin Cancer: An Overview of Epidemiology and Risk Factors,” Seminars in Oncology Nursing, vol. 29, no. 3, pp. 160–169, 2013. Crossref, https://doi.org/10.1016/j.soncn.2013.06.002
[27] A. Sitek, I. Rosset, E. Zadzińska, A. Kasielska-Trojan, A. Neskoromna-Jędrzejczak, and B. Antoszewski, “Skin Color Parameters and Fitzpatrick Phototypes in Estimating the Risk of Skin Cancer: A Case-Control Study in the Polish Population,” Journal of the American Academy of Dermatology, vol. 74, no. 4, pp. 716–723, 2016. Crossref, https://doi.org/10.1016/j.jaad.2015.10.022
[28] E. Guerra-rosas and J. Álvarez-borrego, “Methodology for Diagnosing of Skin Cancer on Images of Dermatologic Spots by Spectral Analysis,” Biomedical Optics Express, vol. 6, no. 10, pp. 3876–3891, 2015. Crossref, https://doi.org/10.1364/BOE.6.003876
[29] E. Tuba, I. Ribic, R. Capor-Hrosik, and M. Tuba, “Support Vector Machine Optimized by Elephant Herding Algorithm for Erythemato-Squamous Diseases Detection,” Procedia Computer Science, vol. 122, pp. 916–923, 2017. Crossref, https://doi.org/10.1016/j.procs.2017.11.455
[30] J. K.Williams, D.A.Ahijevych, C.J.Kessinger, T.R.Saxen, M.Steiner, and S.Dettling, “A Machine Learning Approach to Finding Weather Regimes and Skillful Predictor Combinations for Short-Term Storm Forecasting,” National Center for Atmospheric Research, pp. 1–6, 2008.
[31] J. Rocklöv, K. Ebi, and B. Forsberg, “Mortality Related to Temperature and Persistent Extreme Temperatures: A Study of Cause-Specific and Age-Stratified Mortality,” Occupational and Environmental Medicine, vol. 68, no. 7, pp. 531–536, 2011. Crossref, https://doi.org/10.1136/oem.2010.058818