Breast cancer incidences is steadily increasing in Fiji and accurate forecasting can have major implications in controlling this deadly illness. The best forecasting model is the one that does not underestimates or overestimates the true number of breast cancer cases and gives minimal prediction errors. This paper proposes Linear Regression model for forecasting breast cancer cases using the reported number of cases in Fijian population from the year 1995 to 2016. The proposed model is also compared with the Naïve Forecast Method as the benchmark. The performances of the two models were analyzed based on measures such as the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The proposed model was then further validated using the diagnostic measures such as Goodness-of-fit (R 2 ), Tracking Signal (TS) and Bias. The results showed that the proposed Linear Regression model outperformed the Naïve Forecast Method. It also satisfies the validity diagnostic measures and is a better tool in forecasting Fiji's yearly breast cancer cases.
A slight change in sea surface temperature (SST) is a critical condition as too high of SST could cause coral bleaching, which could results in the declining numbers of fish individuals and species on coral reefs. The people of Kiribati, one of the most affected countries by climate change, depend on the reef and ocean for food and economics, thus advance knowledge of SST in the region could benefit the country. In this paper, a multiple linear regression (MLR) is developed for forecasting the sea surface temperature anomaly (SSTA) of the Kiribati Region (7 0 N-15 0 S, 150 0 W-170 0 E) using the Sea Level Pressure Anomaly (SLPA), Air Temperature Anomaly (ATA), Total Cloudiness Anomaly (TCA), Relative Humidity Anomaly (RHA), Wind Eastward component Anomaly (WECA), Wind Northward component Anomaly (WNCA) and Wind Scalar Anomaly (WSA) as predictors. We validate the proposed model and determine which predictors should we include, and to what extend does this model predict the SSTA in the Kiribati region. The proposed model is compared with the Naïve Method by various error functions such as the Root Square Mean Error (RSME), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). We found that ATA, TCA, WNCA, WECA and WSA are the best predictor variables in the forecasting model, which satisfy all the MLR assumptions and performing better than the Naïve method, hence, it may be useful for forecasting SST accurately.
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