A Comparison of The Predictive Ability between Logistic and Gompertz Model on COVID-19 Outbreak

A predictive model can be learned using historical information. Thereafter, information about a running case is combined with a predictive model to estimate the case's remaining flow time. The predictive model is based on data from past events, which can be used to make predictions for current operating situations. For example, the case of coronavirus disease 2019 (COVID-19), which is currently infecting the whole world, including Indonesia, have influenced various aspects, ranging from the educational environment, business, economy, to the companies. Data scientists are urgently needed who can help organizations improve their operational processes. Therefore, this journal discusses the prediction of the peak number of COVID-19 cases in Indonesia, using two prediction models, logistic and Gompertz. The results obtained show that the Gompertz model has higher accuracy than the logistic model, with an accuracy of 99.85%. This journal's results are expected to help organizations estimate the time to rebuild themselves after being affected by COVID-19.

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Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

BMC Medical Research Methodology ◽

10.1186/s12874-021-01321-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Menelaos Pavlou ◽

Gareth Ambler ◽

Rumana Z. Omar

Keyword(s):

Risk Prediction ◽

Cluster Size ◽

Linear Models ◽

Prediction Models ◽

Predictive Accuracy ◽

Clustered Data ◽

Predictor Variable ◽

Simulated Data ◽

Predictive Ability ◽

Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

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A review of literature on risk prediction tools for hospital readmissions in older adults

Journal of Health Organization and Management ◽

10.1108/jhom-11-2020-0450 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Michelle Louise Gatt ◽

Maria Cassar ◽

Sandra C. Buttigieg

Keyword(s):

Risk Prediction ◽

Prediction Models ◽

Hospital Readmissions ◽

Predictive Ability ◽

Chronic Obstructive ◽

Risk Category ◽

Extensive Literature ◽

Content Type ◽

Prediction Tools ◽

Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

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Empirics of Korean Shipping Companies’ Default Predictions

Risks ◽

10.3390/risks9090159 ◽

2021 ◽

Vol 9 (9) ◽

pp. 159

Author(s):

Sunghwa Park ◽

Hyunsok Kim ◽

Janghan Kwon ◽

Taeil Kim

Keyword(s):

Logit Model ◽

Prediction Models ◽

Predictive Ability ◽

Financial Ratios ◽

Probability Of Default ◽

Shipping Industry ◽

Market Participants ◽

Korean Economy ◽

Default Prediction ◽

Financial Characteristics

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

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An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

Weather and Forecasting ◽

10.1175/waf-d-20-0233.1 ◽

2021 ◽

Author(s):

Eva–Maria Walz ◽

Marlon Maranan ◽

Roderick van der Linden ◽

Andreas H. Fink ◽

Peter Knippertz

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Predictive Ability ◽

Window Size ◽

Predictive Performance ◽

Ensemble Forecast ◽

High Mountain ◽

Rainfall Occurrence ◽

Precipitation Measurement ◽

Global Precipitation

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

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Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

Cancer Cell International ◽

10.1186/s12935-021-02424-7 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Tenghui Han ◽

Jun Zhu ◽

Xiaoping Chen ◽

Rujie Chen ◽

Yu Jiang ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Liver Metastasis ◽

Predictive Model ◽

Area Under The Curve ◽

Predictive Ability ◽

Subsequent Treatment ◽

Training Dataset ◽

Discriminative Ability ◽

Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

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Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽

10.1161/str.46.suppl_1.wmp37 ◽

2015 ◽

Vol 46 (suppl_1) ◽

Author(s):

Blessing Jaja ◽

Hester Lingsma ◽

Ewout Steyerberg ◽

R. Loch Macdonald ◽

Keyword(s):

Subarachnoid Hemorrhage ◽

Cross Validation ◽

Prediction Models ◽

Aneurysmal Subarachnoid Hemorrhage ◽

Model Performance ◽

Predictor Variable ◽

Predictive Ability ◽

Prognostic Scores ◽

Operating Characteristics ◽

Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

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Business Distress Prediction Using Bayesian Logistic Model for Indian Firms

Risks ◽

10.3390/risks6040113 ◽

2018 ◽

Vol 6 (4) ◽

pp. 113 ◽

Cited By ~ 1

Author(s):

Arvind Shrivastava ◽

Kuldeep Kumar ◽

Nitin Kumar

Keyword(s):

Bayesian Modeling ◽

Logistic Model ◽

Predictive Ability ◽

Emerging Economy ◽

Predictive Capability ◽

Corporate Sector ◽

Distress Prediction ◽

Out Of Sample ◽

Distressed Firms ◽

Corporate Distress

The objective of the study is to perform corporate distress prediction for an emerging economy, such as India, where bankruptcy details of firms are not available. Exhaustive panel dataset extracted from Capital IQ has been employed for the purpose. Foremost, the study contributes by devising novel framework to capture incipient signs of distress for Indian firms by employing a combination of firm specific parameters. The strategy not only enables enlarging the sample of distressed firms but also enables to obtain robust results. The analysis applies both standard Logistic and Bayesian modeling to predict distressed firms in Indian corporate sector. Thereby, a comparison of predictive ability of the two approaches has been carried out. Both in-sample and out of sample evaluation reveal a consistently better predictive capability employing Bayesian methodology. The study provides useful structure to indicate the early signals of failure in Indian corporate sector that is otherwise limited in literature.

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Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2019070104 ◽

2019 ◽

Vol 14 (3) ◽

pp. 40-57

Author(s):

Yazan Alnsour ◽

Rassule Hadidi ◽

Neetu Singh

Keyword(s):

Hospital Mortality ◽

Predictive Model ◽

Health Care Providers ◽

Prediction Models ◽

Predictive Analytics ◽

Area Under The Curve ◽

The United States ◽

Hospital Death ◽

Care Providers ◽

Chi Square

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

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Temporal progress of southern rust in maize under different environmental conditions

Fitopatologia Brasileira ◽

10.1590/s0100-41582003000300008 ◽

2003 ◽

Vol 28 (3) ◽

pp. 273-278 ◽

Cited By ~ 10

Author(s):

Cláudia V. Godoy ◽

Lílian Amorim ◽

Armando Bergamin Filho ◽

Herbert P. Silva ◽

Willian J. Silva ◽

...

Keyword(s):

Relative Humidity ◽

Disease Severity ◽

Logistic Model ◽

Inflection Point ◽

Gompertz Model ◽

Disease Progress Curve ◽

Disease Progress ◽

Progress Curve ◽

Puccinia Polysora ◽

The Mean

The progress of the severity of southern rust in maize (Zea mays) caused by Puccinia polysora was quantified in staggered plantings in different geographical areas in Brazil, from October to May, over two years (1995-1996 and 1996-1997). The logistic model, fitted to the data, better described the disease progress curves than the Gompertz model. Four components of the disease progress curves (maximum disease severity; area under the disease progress curve, AUDPC; area under the disease progress curve around the inflection point, AUDPCi; and epidemic rate) were used to compare the epidemics in different areas and at different times of planting. The AUDPC, AUDPCi, and the epidemic rate were analyzed in relation to the weather (temperature, relative humidity, hours of relative humidity >90%, and rainfall) and recorded during the trials. Disease severity reached levels greater than 30% in Piracicaba and Guaíra in the plantings between December and January. Lower values of AUDPC occurred in later plantings at both locations. The epidemic rate was positively correlated (P < 0.05) with the mean daily temperatures and negatively correlated with hours of relative humidity >90%. The AUDPC was not correlated with any weather variable. The AUDPCi was negatively related to both variables connected to humidity, but not to rain. Long periods (mostly >13 h day-1) of relative humidity >90% (that corresponded to leaf wetness) occurred in Castro. Severity of southern rust in maize has always been low in Castro, thus the negative correlations between disease and the two humidity variables.

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Prediction of Shell Egg Price-A Shortrun Model

Journal of Agricultural and Applied Economics ◽

10.1017/s008130520001030x ◽

1971 ◽

Vol 3 (1) ◽

pp. 175-179

Author(s):

Sujit K. Roy

Keyword(s):

Predictive Model ◽

Econometric Model ◽

Predictive Ability ◽

Alternative Methods ◽

Quantitative Models

Variations in price occur frequently in the U. S. shell egg industry and, consequently, interest has been widespread among the traders and the producers with regard to such price variations. Quantitative models for the specific purpose of predicting shortrun egg price have so far been conspicuously absent. The basic objectives of this discussion are first, to present an econometric model to predict quarterly shell egg price and, second, to explain the casual factors which appear to affect price in the immediate future. Finally, alternative methods of estimating the predictive model have been compared with regard to their relative predictive ability.

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