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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 13 Issue 12 ijcatr13121004

IJCATR Volume 13 Issue 12

Statistical Analysis of Key Factors Influencing Leptospirosis Disease in Sri Lanka

AGR Sandeepa, S Dayarathne
10.7753/IJCATR1312.1004
keywords : Leptospirosis, Chi-squared test, Logistic regression, Clinical variables, Statistical methods, Predictive model

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In Sri Lanka, Leptospirosis is among the leading public health concerns due to its high morbidity and wide spectrum of clinical manifestations. This study uses data from hospitals all over Sri Lanka, from 2016 to 2019, to investigate the main determinants influencing the outcome of Leptospirosis. Statistical methods, such as logistic regression and chi-squared tests, have been used in order to examine the relationship between disease outcome and various clinical and demographic variables. The findings show a remarkable association between certain symptoms, diagnostic variables, and outcomes. Neck stiffness, leukocyte count, and diagnostic methods were the critical predictors established through logistic regression analysis. Although the predictive model has moderate accuracy 43.6% and an AUC of 0.546, it promises individually targeted interventions and better disease management in endemic areas. The present study underscores the importance of evidence-based approaches for the reduction of the leptospirosis burden in Sri Lanka.
@artical{a13122024ijcatr13121004,
Title = "Statistical Analysis of Key Factors Influencing Leptospirosis Disease in Sri Lanka",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="12",
Pages ="19 - 25",
Year = "2024",
Authors ="AGR Sandeepa, S Dayarathne"}
  • The study identifies significant predictors of leptospirosis using logistic regression models.
  • Demographic, clinical, and diagnostic factors influencing leptospirosis outcomes are analyzed.
  • R and Excel are utilized for data preprocessing, statistical analysis, and visualization.
  • Model demonstrated a moderate predictive performance, the accuracy was 43.5%.
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