Ensemble Learning For Imbalanced Data Classification Problem

Chujai, Pasapitch; Chomboon, Kittipong; Teerarassamee, Pongsakorn; Kerdprasop, Nittaya; Kerdprasop, Kittisak

doi:10.12792/iciae2015.079

Cited by 19 publications

(16 citation statements)

References 14 publications

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“…More sophisticated sampling methods, such as synthesizing new variants of the existing cases ( 11 ), are also popular. Ensemble methods ( 12 ) can be utilized wherein multiple models use the same minority class cases while each model works with distinct subsets of majority class cases. These trained models are then ensembled into one final classifier that combines their respective predictions into a final prediction.…”

Section: Resultsmentioning

confidence: 99%

Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

et al. 2021

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Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior.Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions.Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.

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Section: Resultsmentioning

confidence: 99%

Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

et al. 2021

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“…Moreover, some of these do have a cyclic order also. However, it is not compulsory to have a pattern in the time series model [17]. The ARIMA model has three parameters: p is the order of AR, d is the degree of difference and q is the order of MA.…”

Section: Model Constructionmentioning

confidence: 99%

Forecasting the health expenditures in Iran by Using Time Series Analysis as Machine Learning Model in Python

Hedayati

Asl

2023

Preprint

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Background: Forecasting the future trend of health expenditures is an important step to achieve universal health coverage (UHC) by 2030. This study aimed to examine the two decades underlying historical trends in Iran’s health expenditures and explore these data as the grounds for forecasting its health expenditures. Methods: Iran’s health spending data spanning 2001–2019 were extracted from the National Health Account 2020 database. Total health expenditure, government, prepaid private, and out-of-pocket spending per capita were forecasted for 2020–2030. The Autoregressive integrated moving average (ARIMA) model was used to obtain future projections based on time series analysis. Python programming language has been used for prediction. Results: The model of total health expenditure (THE), government health expenditure (GHE), social health expenditure (SHE), prepaid private health expenditure (PPHE), and out-of-pocket health expenditure (OOP) are ARIMA (0,2,2), ARIMA (1,2,3), ARIMA (1,2,2), ARIMA (1,1,2), and ARIMA (1,2,0). According to the simulation results, in 2030, Iran’s THE is expected to reach 16,555,761,416 billion IRR, and the constituent ratios in GHE, SHE, PPHE, and OOP will be 29%, 28%, 11%, and 32%, respectively. Conclusions: Forecasting health expenditures is important in ensuring sustainable financing for the health sector, which is a critical aspect of achieving UHC. Our result would help policymakers to make sufficient data available in order to plan the future of resources and estimate the amount of the budget allocated to Healthcare. Iran should take effective measures to control the excessive growth of THE, keep decreasing the OOP percentage, and improve the efficiency and fairness of the use of health funds.

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“…ARIMA was modeled on household electric power consumption and was noted to be more appropriate in short-term forecasting then some other models (Chujai, Kerdprasop and Kerdprasop, 2013). Similarly, A large number of investors have little knowledge on analytics and good prediction of share and equity prices that could help optimize their return on investment.…”

Section: Review Of Literaturementioning

confidence: 99%

Forecasting Daily Equity Price Using Auto Regressive Integrated Moving Average (ARIMA) Model: An Application to Shirpur Gold Refinery Ltd., India

Pradhan

2020

Abhigyan

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Ensemble Learning For Imbalanced Data Classification Problem

Cited by 19 publications

References 14 publications

Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

Forecasting the health expenditures in Iran by Using Time Series Analysis as Machine Learning Model in Python

Forecasting Daily Equity Price Using Auto Regressive Integrated Moving Average (ARIMA) Model: An Application to Shirpur Gold Refinery Ltd., India

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