Abstract
The major challenge in managing blood products lies in the uncertainty of blood demand and supply, with a trade-off between shortage and wastage, especially in most developing countries. Thus, reliable demand predictions can be imperative in planning voluntary blood donation campaigns and improving blood availability within Ghana hospitals. However, most historical datasets on blood demand in Ghana are predominantly contaminated with missing values and outliers due to improper database management systems. Consequently, time-series prediction can be challenging since data cleaning can affect models’ predictive power. Also, machine learning (ML) models’ predictive power for backcasting past years’ lost data is understudied compared to their forecasting abilities. This study thus aims to compare K-Nearest Neighbour regression (KNN), Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) models via a rolling-origin strategy, for forecasting and backcasting a blood demand data with missing values and outliers from a government hospital in Ghana. KNN performed well in forecasting blood demand (12.55% error); whereas, ELM achieved the highest backcasting power (19.36% error). Future studies can also employ ML algorithms as a good alternative for backcasting past values of time-series data that are time-reversible.
Highlights
The non-seasonal autoregressive integrated moving average (ARIMA) model was considered a baseline model for comparison with the machine learning (ML) models for forecasting and backcasting the units of blood demanded via the proposed 18-month rolling-origin strategy with 17 different sets of predictions; and 17 different fitted ARIMA models
The six ML algorithms (KNN, Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM)) were fitted to forecast and backcast the units of blood demanded at the Tema General Hospital via the rolling-origin strategy
The classical non-seasonal ARIMA model was used as a baseline model for comparison with the ML models (KNN, NNAR, GRNN, MLP, ELM and LSTM algorithms), and a proposed rolling-origin strategy was employed for model evaluations
Summary
Blood supply chain (BSC) encapsulates all the processes of collecting, testing, processing, storing and distributing blood and its components from donor to recipient patient (Osorio, Brailsford, & Smith, 2015; Stanger, Wilding, Yates, & Cotton, 2012). Blood cannot be manufactured artificially, and supply depends on voluntary human donors who cannot be predicted.
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