Prediction of COVID-19 using machine learning techniques

Patibandla, R. S. M. Lakshmi; Rao, B. Tarakeswara; Narayana, V. Lakshman

doi:10.1016/b978-0-12-824145-5.00007-1

Cited by 12 publications

(2 citation statements)

References 3 publications

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“…Using the Cat boost model, a feature importance analysis was conducted to identify the most important demographic and socioeconomic risk factors that in uence IPV against married women in Ethiopia. The bene t of implementing a model-based method is that it is more directly associated with model performance and may be able to incorporate the correlation structure between predictors into the signi cance assessment [41].…”

Section: Resultsmentioning

confidence: 99%

Characterizing Intimate Partner Violence against Married Women in Ethiopia with Machine Learning

Mengesha,

Abuhay,

Mamuye

2023

Preprint

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Background Intimate Partner Violence (IPV) is the most common form of violence against women worldwide, perpetrated mostly by intimate partners (men) against women. According to the 2020 United Nations report, 47000 women were killed by their intimate partners or other family members globally. IPV can be categorized as physical, psychological, or sexual violence which may cause a serious and sometimes fatal occurrence. Objective The main goal of this study is to characterize IPV against married women in Ethiopia by developing a machine learning model to 1) predict types of IPV, 2) identify risk factors of IPV, and 3) extract relevant rules for evidence-based strategy and policy formulation to prevent, reduce and control IPV against married women in Ethiopia. Methods The study was conducted following an experimental research approach. The data were gathered from the Ethiopian demographic health survey and preprocessed to make it suitable for the machine learning algorithm. Extreme gradient boosting, Cat boost, Random Forest, and Light GBM with one versus rest class decomposition were employed to develop an IPV prediction model. For building the proposed model a total of 31110 instances with 22 features, and 80/20 training and testing dataset split ratio were used. Result Performance evaluation metrics were used to choose the best-performing algorithm. The overall accuracy of extreme gradient boosting, Cat boost, RF, and Light GBM is 92.57%, 94.39%, 92.94%, and 90.30%, respectively. Feature importance analysis was implemented to identify risk factors of IVP again married women in Ethiopia. Conclusion Cat boost algorithms registered the highest performance with 94.39% accuracy. The most determinant risk factors of intimate partner violence against married women in Ethiopia are women’s household empowerment status, working status, place of residence, first marriage decision, and husband's education level, to mention a few. Relevant rules that may support evidence-driven strategies and policies to prevent, reduce and control IPV against married women in Ethiopia were also extracted from the best performing algorithm.

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Section: Resultsmentioning

confidence: 99%

Characterizing Intimate Partner Violence against Married Women in Ethiopia with Machine Learning

Mengesha,

Abuhay,

Mamuye

2023

Preprint

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“…The prediction models provide the insights that result in tangible outcomes. The predictive statistical approach for disease research work found in the literature deals with ARIMA models [30,31], polynomial regression and hierarchical polynomial regression models [32][33][34]. Recall that a process Y t is said to be ARIMA(p, d, q) if…”

Section: Regression and Forecasting Modelsmentioning

confidence: 99%

Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte

Manou-Abi¹,

Slaoui

Balicchi

2022

Front. Appl. Math. Stat.

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We study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confirmed. The best-fit model of the non negative serial interval distribution is given by a mixture of two Gamma distributions with different shapes and rates. Thus, we propose a modified version of the generation time function of the package R0. Second, we estimate the time-varying reproduction number in Mayotte. Using a justified mathematical learning model, we estimate the transmission parameters range values during the outbreak together with a sensitivity analysis. Finally, using some regression and forecasting methods, we give some learning models of the hospitalized, intensive care, and death cases over a given period. We end with a discussion and the limit of this study together with some forthcoming theoretical developments.

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