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International Journal of Information System Modeling and Design ; 13(9), 2022.
Article in English | Scopus | ID: covidwho-1975017


Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis. © 2019 American Society of Mechanical Engineers (ASME). All rights reserved.

BMC Med Res Methodol ; 20(1): 65, 2020 03 14.
Article in English | MEDLINE | ID: covidwho-1455916


BACKGROUND: Sero- prevalence studies often have a problem of missing data. Few studies report the proportion of missing data and even fewer describe the methods used to adjust the results for missing data. The objective of this review was to determine the analytical methods used for analysis in HIV surveys with missing data. METHODS: We searched for population, demographic and cross-sectional surveys of HIV published from January 2000 to April 2018 in Pub Med/Medline, Web of Science core collection, Latin American and Caribbean Sciences Literature, Africa-Wide Information and Scopus, and by reviewing references of included articles. All potential abstracts were imported into Covidence and abstracts screened by two independent reviewers using pre-specified criteria. Disagreements were resolved through discussion. A piloted data extraction tool was used to extract data and assess the risk of bias of the eligible studies. Data were analysed through a quantitative approach; variables were presented and summarised using figures and tables. RESULTS: A total of 3426 citations where identified, 194 duplicates removed, 3232 screened and 69 full articles were obtained. Twenty-four studies were included. The response rate for an HIV test of the included studies ranged from 32 to 96% with the major reason for the missing data being refusal to consent for an HIV test. Complete case analysis was the primary method of analysis used, multiple imputations 11(46%) was the most advanced method used, followed by the Heckman's selection model 9(38%). Single Imputation and Instrumental variables method were used in only two studies each, with 13(54%) other different methods used in several studies. Forty-two percent of the studies applied more than two methods in the analysis, with a maximum of 4 methods per study. Only 6(25%) studies conducted a sensitivity analysis, while 11(46%) studies had a significant change of estimates after adjusting for missing data. CONCLUSION: Missing data in survey studies is still a problem in disease estimation. Our review outlined a number of methods that can be used to adjust for missing data on HIV studies; however, more information and awareness are needed to allow informed choices on which method to be applied for the estimates to be more reliable and representative.

HIV Infections , Research Design , Bias , Cross-Sectional Studies , HIV Infections/diagnosis , HIV Infections/epidemiology , Humans , Prevalence