Forecasting the incidence rates of top three cancers in Malaysia

Lazam, Norazliani Md; Shair, Syazreen Niza; Asmuni, Nurin Haniah; Jamaludin, Aisyah; Yusri, Ardinei Ahmad

doi:10.1063/5.0110929

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…15 A study comparing ARIMA and ExpSmoothing found that the MAPE of ExpSmoothing is lower than that of ARIMA. 22 A study compared the performance of SSM, PF, and GSMs including LLM and LQM to predict mortality counts in cancer patients and showed that MSE is low for both SSM and Local Quadratic Model, indicating that both perform equally well. 38 The study demonstrated that the LQM and the SSM are able to adjust to the variations in the data better than LLM and PF.…”

Section: Discussionmentioning

confidence: 99%

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A Systematic Review of Cancer Burden Forecasting Models: Evaluating Efficacy for Long-Term Predictions Using Annual Data

Dahia,

Konduru,

Barreto

2024

Preprint

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This paper presents a comprehensive systematic review of forecasting models applied to cancer burden prediction, focusing on their efficacy for long-term predictions using annual data. Cancer represents a significant challenge to global healthcare systems, necessitating accurate forecasting models for effective planning and resource allocation. We evaluated various methodologies, including JoinPoint Regression, Age-Period-Cohort models, time series analysis, exponential smoothing, machine learning, and more, highlighting their strengths and weaknesses in forecasting cancer incidence, mortality, and Disability-Adjusted Life Years. Our literature search strategy involved a systematic search across major scientific databases, yielding a final selection of 10 studies for in-depth analysis. These studies employed diverse forecasting models, which were critically assessed for their predictive accuracy, handling of annual data limitations, and applicability to cancer epidemiology. Our findings indicate that no single model universally excels in all aspects of cancer burden forecasting. However, ARIMA models and their variants consistently demonstrated strong predictive performance across different cancers, countries, and projection periods. The evaluation also underscores the challenges posed by limited long-term data and the potential for complex models to overfit in sparse data scenarios. Importantly, the review suggests a need for further research into developing models capable of accurate longer-term forecasts, which could significantly enhance healthcare planning and intervention strategies. In conclusion, while ARIMA and its derivatives currently lead in performance, there is a pressing need for innovative models that extend predictive capabilities over longer horizons, improving the global healthcare sector's response to the cancer burden.

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Section: Discussionmentioning

confidence: 99%

“…The mathematical structure of the ARIMAX model is based on the ARIMA model, with the addition of exogenous variables. 18 We reviewed 5 studies which used ARIMA 15,[19][20][21][22] and 1 study which used ARIMAX. 23 The description of the studies employing ARIMA/ARIMAX can be found in Table 1.…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

A Systematic Review of Cancer Burden Forecasting Models: Evaluating Efficacy for Long-Term Predictions Using Annual Data

Dahia,

Konduru,

Barreto

2024

Preprint

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Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

Bouzon Nagem Assad,

Gomes Ferreira da Costa,

Spiegel

et al. 2024

Sci Rep

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The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

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Forecasting the incidence rates of top three cancers in Malaysia

Cited by 2 publications

References 8 publications

A Systematic Review of Cancer Burden Forecasting Models: Evaluating Efficacy for Long-Term Predictions Using Annual Data

A Systematic Review of Cancer Burden Forecasting Models: Evaluating Efficacy for Long-Term Predictions Using Annual Data

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

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