Forecasting of infection prevalence of helicobacter pylori using regression analysis
Global warming may have a significant impact on human health because of the growth of the population of harmful bacteria such as Helicobacter pylori infection. It is crucial to predict the prevalence of a pathogen in a society in a faster and more cost-effective way in order to manage caused disease...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IIUM, Malaysia
2022
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45747/ |
| _version_ | 1848827500550946816 |
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| author | Usarov, Komiljon Ahmedov, Anvarjon A. Abasiyanik, Mustafa Fatih Ku Muhammad Naim, Ku Khalif |
| author_facet | Usarov, Komiljon Ahmedov, Anvarjon A. Abasiyanik, Mustafa Fatih Ku Muhammad Naim, Ku Khalif |
| author_sort | Usarov, Komiljon |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Global warming may have a significant impact on human health because of the growth of the population of harmful bacteria such as Helicobacter pylori infection. It is crucial to predict the prevalence of a pathogen in a society in a faster and more cost-effective way in order to manage caused disease. In this research, we have done predictive analysis of H. pylori infection spread behavior with respect to weather parameters (e.g., humidity, dew point, temperature, pressure, and wind speed) of Istanbul based on a database from Istanbul Samatya Hospital. We developed a forecasting model to predict H. pylori infection prevalence. The goal is to develop a machine learning model to predict H. pylori (Hp) related infection diseases (e.g., gastric ulcer diseases, gastritis) based on climate variables. The dataset for this study covered years from 1999 to 2003 and contained a total of 7014 rows from the Samatya Hospital in Istanbul. The weather information related to those years and location, including humidity (H), dew point (D), temperature (T), pressure (P) and wind speed (W), were collected from the following website: https://www.wunderground.com. In this paper we analyzed the forecasting model, which was used to predict H. pylori infection prevalence, by non-linear multivariate linear regression model (MLRM). We applied the non-linear least square method of minimization for the sum of squares to find optimal parameters of MLRM. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the Hp infection disease is most influenced by humidity. Hp prevalence is modelled using the Multiple Regression Method equation, the average H, D, T, P, and W were the most important parameters to deviation of the datasets (testing dataset was 17% and 18% for training dataset). This showed that the statistical model predicts the Hp prevalence with about 83% accuracy of the testing data set (11 months) and 87% accuracy of the training data set (42 months). Based on the proposed model, monthly infection can be predicted early for medical services to take preventative measures and for government to prepare against the bacteria. In addition, drug producers can adjust their drug production rates based on forecasting results. |
| first_indexed | 2025-11-15T04:01:42Z |
| format | Article |
| id | ump-45747 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:01:42Z |
| publishDate | 2022 |
| publisher | IIUM, Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-457472025-09-29T01:54:38Z https://umpir.ump.edu.my/id/eprint/45747/ Forecasting of infection prevalence of helicobacter pylori using regression analysis Usarov, Komiljon Ahmedov, Anvarjon A. Abasiyanik, Mustafa Fatih Ku Muhammad Naim, Ku Khalif QA75 Electronic computers. Computer science QR Microbiology Global warming may have a significant impact on human health because of the growth of the population of harmful bacteria such as Helicobacter pylori infection. It is crucial to predict the prevalence of a pathogen in a society in a faster and more cost-effective way in order to manage caused disease. In this research, we have done predictive analysis of H. pylori infection spread behavior with respect to weather parameters (e.g., humidity, dew point, temperature, pressure, and wind speed) of Istanbul based on a database from Istanbul Samatya Hospital. We developed a forecasting model to predict H. pylori infection prevalence. The goal is to develop a machine learning model to predict H. pylori (Hp) related infection diseases (e.g., gastric ulcer diseases, gastritis) based on climate variables. The dataset for this study covered years from 1999 to 2003 and contained a total of 7014 rows from the Samatya Hospital in Istanbul. The weather information related to those years and location, including humidity (H), dew point (D), temperature (T), pressure (P) and wind speed (W), were collected from the following website: https://www.wunderground.com. In this paper we analyzed the forecasting model, which was used to predict H. pylori infection prevalence, by non-linear multivariate linear regression model (MLRM). We applied the non-linear least square method of minimization for the sum of squares to find optimal parameters of MLRM. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the Hp infection disease is most influenced by humidity. Hp prevalence is modelled using the Multiple Regression Method equation, the average H, D, T, P, and W were the most important parameters to deviation of the datasets (testing dataset was 17% and 18% for training dataset). This showed that the statistical model predicts the Hp prevalence with about 83% accuracy of the testing data set (11 months) and 87% accuracy of the training data set (42 months). Based on the proposed model, monthly infection can be predicted early for medical services to take preventative measures and for government to prepare against the bacteria. In addition, drug producers can adjust their drug production rates based on forecasting results. IIUM, Malaysia 2022 Article PeerReviewed pdf en cc_by_nc_4 https://umpir.ump.edu.my/id/eprint/45747/1/Forecasting%20of%20infection%20prevalence%20of%20helicobacter%20pylori.pdf Usarov, Komiljon and Ahmedov, Anvarjon A. and Abasiyanik, Mustafa Fatih and Ku Muhammad Naim, Ku Khalif (2022) Forecasting of infection prevalence of helicobacter pylori using regression analysis. IIUM Engineering Journal, 23 (2). pp. 183-192. ISSN 1511-788X. (Published) https://doi.org/10.31436/iiumej.v23i2.2164 https://doi.org/10.31436/iiumej.v23i2.2164 https://doi.org/10.31436/iiumej.v23i2.2164 |
| spellingShingle | QA75 Electronic computers. Computer science QR Microbiology Usarov, Komiljon Ahmedov, Anvarjon A. Abasiyanik, Mustafa Fatih Ku Muhammad Naim, Ku Khalif Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title | Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title_full | Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title_fullStr | Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title_full_unstemmed | Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title_short | Forecasting of infection prevalence of helicobacter pylori using regression analysis |
| title_sort | forecasting of infection prevalence of helicobacter pylori using regression analysis |
| topic | QA75 Electronic computers. Computer science QR Microbiology |
| url | https://umpir.ump.edu.my/id/eprint/45747/ https://umpir.ump.edu.my/id/eprint/45747/ https://umpir.ump.edu.my/id/eprint/45747/ |