Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Inaccurate prediction would cause the insurance company encounter catastrophic losses and may lead to overpriced premiums where low-earning consumers cannot afford to insure themselves. The ability to forecast mortality rates accurately can allow the insurance company to take preventive measures to introduce new policies with reasonable prices. In this paper, several Lee–Carter (LC) based models are used to forecast the mortality rates in a case study of the Malaysian population. The LC-ARIMA model and also a combination of the LC model with two machine learning (ML) methods, namely the random forest (RF) and artificial neural network (ANN) methods are utilized on the prediction of mortality rates for males and females in Malaysia, whereby the LC-Random Forest (LC-RF) hybrid model is a new model that is introduced in this paper. Seventeen years of mortality data in Malaysia are selected as the dataset for this research. To analyze how the forecasting models perform for other countries, we have determined the model that has the best fit and produced the best forecasted mortality rates for all the other countries that are studied. This research has showed that LC-ANN and LC-ARIMA are the best model in predicting the mortality rates of males and females in Malaysia, respectively. This study has also found that the LC-ARIMA model is the best performing model in forecasting the mortality rates in countries that have longer life expectancy and a good healthcare system such as Sweden, Ireland, Japan, Hong Kong, Norway, Switzerland and Czechia. In contrast, the LC-ANN model is the best performing model in forecasting the mortality rates in countries that have a less efficiency, less accessibility healthcare system, and bad personal behavior such as Malaysia, Canada and Latvia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it