Afsana Al Sharmin, H. S. Zulkafli, Nazihah Mohamed Ali
{"title":"ESTABLISHING CUT-OFF POINTS FOR CONSISTENCY IN REPORTING HYPOGLYCEMIA SYMPTOMS AMONG DIABETES PATIENTS","authors":"Afsana Al Sharmin, H. S. Zulkafli, Nazihah Mohamed Ali","doi":"10.17654/0973514324004","DOIUrl":"https://doi.org/10.17654/0973514324004","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This is a descriptive, cross-sectional study to analyze the effect of alcohol and smoking in the hemoglobin present in blood and determining the other factors that affect it. The data was obtained from the national health insurance service in Korea. The multiple linear regression model was performed on the sample size of 65535 individuals, which contain adults aged between 20 to 85 years of both males and females in Korea. This sample covers people who smoke and drink during their lifetime. There is a statistically significant effect of the explanatory variables (Sex, Age, Height, Weight, Smoking state, Drinking state) on the dependent variable (Hemoglobin), with F-stat (10325.983) and P-value (0.000) at 5% level of significant. The variance inflation factor (VIF) ranged between (1.280 to 3.327); is less than 5; which means that there is no collinearity. Also, the R squared (0.486) is less than Durbin Watson statistic (2.006) which means this model is not spurious suggesting that there is no autocorrelation, or partial correlation in the data. The explanatory variables explain 48.6% of the total variation in hemoglobin levels in the blood. Received: September 7, 2023 Accepted: November 2, 2023
{"title":"STATISTICAL ANALYSIS STUDYING THE FACTORS AFFECTING HEMOGLOBIN","authors":"Maysoon A. Sultan","doi":"10.17654/0973514324003","DOIUrl":"https://doi.org/10.17654/0973514324003","url":null,"abstract":"This is a descriptive, cross-sectional study to analyze the effect of alcohol and smoking in the hemoglobin present in blood and determining the other factors that affect it. The data was obtained from the national health insurance service in Korea. The multiple linear regression model was performed on the sample size of 65535 individuals, which contain adults aged between 20 to 85 years of both males and females in Korea. This sample covers people who smoke and drink during their lifetime. There is a statistically significant effect of the explanatory variables (Sex, Age, Height, Weight, Smoking state, Drinking state) on the dependent variable (Hemoglobin), with F-stat (10325.983) and P-value (0.000) at 5% level of significant. The variance inflation factor (VIF) ranged between (1.280 to 3.327); is less than 5; which means that there is no collinearity. Also, the R squared (0.486) is less than Durbin Watson statistic (2.006) which means this model is not spurious suggesting that there is no autocorrelation, or partial correlation in the data. The explanatory variables explain 48.6% of the total variation in hemoglobin levels in the blood. Received: September 7, 2023 Accepted: November 2, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using the example of the skin glands of adult female Norway rats, a matrix of gray shades has been developed using the Python programming language, each element of which corresponds to a certain level of histoenzymatic activity. The matrix is based on the transformation of the sign form of enzyme activity into RGB coordinates, which formed the basis of an array comprising four enzymes (acid phosphatase, alkaline phosphatase, adenosine triphosphatase and peroxidase) for five topographic areas (nape, mouth corners, upper eyelids, anal area and soles of paws). The resulting matrix can give additional visualization to the results, and can also be used in comparative data analysis to solve various biological problems. Received: August 27, 2023Accepted: October 14, 2023
{"title":"MATRIX VISUALIZATION OF THE DEGREES OF HISTOCHEMICAL ACTIVITY OF ENZYMES IN THE SKIN GLANDS OF NORWAY RATS","authors":"A. B. Kiladze, N. K. Dzhemukhadze","doi":"10.17654/0973514324002","DOIUrl":"https://doi.org/10.17654/0973514324002","url":null,"abstract":"Using the example of the skin glands of adult female Norway rats, a matrix of gray shades has been developed using the Python programming language, each element of which corresponds to a certain level of histoenzymatic activity. The matrix is based on the transformation of the sign form of enzyme activity into RGB coordinates, which formed the basis of an array comprising four enzymes (acid phosphatase, alkaline phosphatase, adenosine triphosphatase and peroxidase) for five topographic areas (nape, mouth corners, upper eyelids, anal area and soles of paws). The resulting matrix can give additional visualization to the results, and can also be used in comparative data analysis to solve various biological problems. Received: August 27, 2023Accepted: October 14, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selecting model for classifying target correctly is important. Logistic regression (LR), K-nearest neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB) are base models in classifying target. Tracking ensemble is the method for comparing accuracy in machine learning. Datasets are generated by a code of Python as recommended by Brownlee [1]. Five sample sizes of 1,000, 3,000, 5,000, 7,000, and 10,000 are selected. The number of features is 20 having informative and redundant features, respectively, as 15 and 5. The result shows that support vector machine (SVM) has the highest mean of accuracy and the lowest coefficient of variation of accuracy in all sample sizes. Naïve Bayes (NB) has the lowest mean of accuracy and the highest coefficient of variation of accuracy in all sample sizes. It is recommended to select support vector machine (SVM) for classifying target. Received: August 13, 2023Accepted: October 9, 2023
{"title":"COMPARING ACCURACY OF LOGISTIC REGRESSION, K-NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, AND NAÏVE BAYES MODELS USING TRACKING ENSEMBLE MACHINE LEARNING","authors":"Kuntoro Kuntoro","doi":"10.17654/0973514324001","DOIUrl":"https://doi.org/10.17654/0973514324001","url":null,"abstract":"Selecting model for classifying target correctly is important. Logistic regression (LR), K-nearest neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB) are base models in classifying target. Tracking ensemble is the method for comparing accuracy in machine learning. Datasets are generated by a code of Python as recommended by Brownlee [1]. Five sample sizes of 1,000, 3,000, 5,000, 7,000, and 10,000 are selected. The number of features is 20 having informative and redundant features, respectively, as 15 and 5. The result shows that support vector machine (SVM) has the highest mean of accuracy and the lowest coefficient of variation of accuracy in all sample sizes. Naïve Bayes (NB) has the lowest mean of accuracy and the highest coefficient of variation of accuracy in all sample sizes. It is recommended to select support vector machine (SVM) for classifying target. Received: August 13, 2023Accepted: October 9, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136382199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duckworth’s test is a well-known non-parametric statistical test used for comparing the medians of two populations. However, the conventional Duckworth’s test, based on classical statistics, is inadequate when dealing with data originating from neutrosophic populations. This paper presents a modified version of Duckworth’s test, specifically designed for neutrosophic statistics. This novel approach enables the application of Duckworth’s test to imprecise, uncertain, or data recorded in indeterminate intervals. The proposed test statistic under neutrosophic statistics is introduced and applied to real-world Covid-19 data. Through comprehensive analysis and simulation studies, the efficacy of the proposed Duckworth’s test under neutrosophic statistics is demonstrated to surpass that of the existing Duckworth’s test under classical statistics. Received: August 7, 2023Accepted: September 25, 2023
{"title":"ANALYSIS OF CORONA PATIENTS USING UNCERTAINTY-BASED NON-PARAMETRIC MEDIAN TEST","authors":"Muhammad Aslam, Muhammad Saleem","doi":"10.17654/0973514323018","DOIUrl":"https://doi.org/10.17654/0973514323018","url":null,"abstract":"Duckworth’s test is a well-known non-parametric statistical test used for comparing the medians of two populations. However, the conventional Duckworth’s test, based on classical statistics, is inadequate when dealing with data originating from neutrosophic populations. This paper presents a modified version of Duckworth’s test, specifically designed for neutrosophic statistics. This novel approach enables the application of Duckworth’s test to imprecise, uncertain, or data recorded in indeterminate intervals. The proposed test statistic under neutrosophic statistics is introduced and applied to real-world Covid-19 data. Through comprehensive analysis and simulation studies, the efficacy of the proposed Duckworth’s test under neutrosophic statistics is demonstrated to surpass that of the existing Duckworth’s test under classical statistics. Received: August 7, 2023Accepted: September 25, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Opeyemi P. Ogundile, Hilary I. Okagbue, Akinwumi A. Akinpelu, Adedayo F. Adedotun, Toluwalase J. Akingbade
Background: HIV/AIDS is endemic in Nigeria since the first case was reported in 1986. Several risk factors contribute to its prevalence, and the successive government has devised different programs to halt the spread. Awareness is one of those programs that helps to promote voluntary testing and prevention of HIV. The aim of this paper is to assess the level of awareness of HIV/AIDS among private and public primary school pupils in Ado-Odo, Ota, Southwest Nigeria. Methods: Questionnaire was used as the tool for data collection and p-value < 0.05 was considered significant. Multistage sampling was used to select four primary schools divided into equal numbers of private and public schools. Thereafter, simple random sampling was used to administer the questionnaire to the pupils. The research was conducted in May 2019 and SPSS 23.0 was used in the data analysis. Mediation analysis was used to build the hierarchal models that describe the interrelationship among the variables that was used to measure the level of awareness. Results: Out of 400 questionnaires distributed, 354 representing 88.5% were used for the final analysis. 173 (48.9%) and 181 (51.1%) of the primary school pupils (respondents) were males and females, respectively. The main results are given as follows: The awareness of mode of transmission was the highest and followed by knowledge of preventive measures, general knowledge of HIV/AIDS and knowledge of non-risk factors in descending order. Hierarchical regression analysis yielded two mediation models. Firstly, knowledge of preventive measures mediate the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Secondly, knowledge of non-risk factors mediates the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Conclusion: Awareness of how the infection cannot be transmitted is low which connotes stigmatization. Attitudinal changes are needed and awareness campaigns should be channeled to private primary school. Also, the hierarchical models have provided the link through which possible preventive measures could be explored. Received: June 29, 2023Accepted: July 29, 2023
{"title":"MEDIATORS OF HIV/AIDS AWARENESS AMONG PRIMARY SCHOOL PUPILS IN NIGERIA","authors":"Opeyemi P. Ogundile, Hilary I. Okagbue, Akinwumi A. Akinpelu, Adedayo F. Adedotun, Toluwalase J. Akingbade","doi":"10.17654/0973514323017","DOIUrl":"https://doi.org/10.17654/0973514323017","url":null,"abstract":"Background: HIV/AIDS is endemic in Nigeria since the first case was reported in 1986. Several risk factors contribute to its prevalence, and the successive government has devised different programs to halt the spread. Awareness is one of those programs that helps to promote voluntary testing and prevention of HIV. The aim of this paper is to assess the level of awareness of HIV/AIDS among private and public primary school pupils in Ado-Odo, Ota, Southwest Nigeria. Methods: Questionnaire was used as the tool for data collection and p-value < 0.05 was considered significant. Multistage sampling was used to select four primary schools divided into equal numbers of private and public schools. Thereafter, simple random sampling was used to administer the questionnaire to the pupils. The research was conducted in May 2019 and SPSS 23.0 was used in the data analysis. Mediation analysis was used to build the hierarchal models that describe the interrelationship among the variables that was used to measure the level of awareness. Results: Out of 400 questionnaires distributed, 354 representing 88.5% were used for the final analysis. 173 (48.9%) and 181 (51.1%) of the primary school pupils (respondents) were males and females, respectively. The main results are given as follows: The awareness of mode of transmission was the highest and followed by knowledge of preventive measures, general knowledge of HIV/AIDS and knowledge of non-risk factors in descending order. Hierarchical regression analysis yielded two mediation models. Firstly, knowledge of preventive measures mediate the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Secondly, knowledge of non-risk factors mediates the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Conclusion: Awareness of how the infection cannot be transmitted is low which connotes stigmatization. Attitudinal changes are needed and awareness campaigns should be channeled to private primary school. Also, the hierarchical models have provided the link through which possible preventive measures could be explored. Received: June 29, 2023Accepted: July 29, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abolfazl Payandeh, Habibollah Esmaily, Masoud Salehi, Seyed Mahdi Amir Jahanshahi, Maryam Salari, Seyed Ali Alamdaran, Ahmad Bolouri
Today, there is a high demand for artificial intelligence (AI) applications in distinct areas of research. AI can be used in the medical context to help in clinical decision-making and limited resource allocation. The present study proposes the best model for the detection of COVID-19, the prediction of disease in new cases, and also determines the top significant features related to COVID-19, using DL algorithms as a subset of AI techniques. In this retrospective population-based study, 10862 individuals suspicious of COVID-19 participated. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran, from 20 February 2020 to 21 June 2021. We employed artificial neural networks (ANN), random forests (RF), decision tree (DT), support vector machines (SVM), boosted trees (BT), and logistic regression (LR) DL algorithms. Our findings indicated that the RF model had higher performance than all other algorithms. The RF algorithm had a sensitivity of 66%, specificity of 95%, precision of 88%, accuracy of 85%, and AUC of 74%. Our study found that the common top predictors for detecting COVID-19 were: age, SpO2, reception season, CT result, contact history, sex, and fever. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings. Received: August 1, 2023Accepted: September 4, 2023
{"title":"A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY","authors":"Abolfazl Payandeh, Habibollah Esmaily, Masoud Salehi, Seyed Mahdi Amir Jahanshahi, Maryam Salari, Seyed Ali Alamdaran, Ahmad Bolouri","doi":"10.17654/0973514323016","DOIUrl":"https://doi.org/10.17654/0973514323016","url":null,"abstract":"Today, there is a high demand for artificial intelligence (AI) applications in distinct areas of research. AI can be used in the medical context to help in clinical decision-making and limited resource allocation. The present study proposes the best model for the detection of COVID-19, the prediction of disease in new cases, and also determines the top significant features related to COVID-19, using DL algorithms as a subset of AI techniques. In this retrospective population-based study, 10862 individuals suspicious of COVID-19 participated. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran, from 20 February 2020 to 21 June 2021. We employed artificial neural networks (ANN), random forests (RF), decision tree (DT), support vector machines (SVM), boosted trees (BT), and logistic regression (LR) DL algorithms. Our findings indicated that the RF model had higher performance than all other algorithms. The RF algorithm had a sensitivity of 66%, specificity of 95%, precision of 88%, accuracy of 85%, and AUC of 74%. Our study found that the common top predictors for detecting COVID-19 were: age, SpO2, reception season, CT result, contact history, sex, and fever. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings. Received: August 1, 2023Accepted: September 4, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135831139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"COMPUTATIONAL STATISTICS AND DATA ANALYSIS TO DETERMINE FINANCIAL AND ECONOMICAL IMPACTS OF COVID-19","authors":"A. T. Abdulrahman","doi":"10.17654/0973514323014","DOIUrl":"https://doi.org/10.17654/0973514323014","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44021649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles K. Mutai, P. McSharry, I. Ngaruye, E. Musabanganji
{"title":"TEMPORAL TRENDS OF HIV PREVALENCE IN SUB-SAHARAN AFRICA","authors":"Charles K. Mutai, P. McSharry, I. Ngaruye, E. Musabanganji","doi":"10.17654/0973514323015","DOIUrl":"https://doi.org/10.17654/0973514323015","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45491404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Elbatal, Safar M. Alghamdi, A. Ghorbal, A. W. Shawki
{"title":"EXPONENTIATED KAVYA-MANOHARAN BURR X DISTRIBUTION: ESTIMATION UNDER CENSORED TYPE II WITH APPLICATIONS IN MEDICAL DATA","authors":"I. Elbatal, Safar M. Alghamdi, A. Ghorbal, A. W. Shawki","doi":"10.17654/0973514323013","DOIUrl":"https://doi.org/10.17654/0973514323013","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67808126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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