Analysis of a Time Series in a Tertiary Care Center for Non-Hodgkin Lymphoma
Keywords:
non-Hodgkin lymphoma, temporal series, machine learning.Abstract
Introduction: The incidence of non-Hodgkin lymphoma increases between 1 and 2% each year. Ordering its monthly incidence chronologically allows it to analyze characteristics inherent to a time series.
Objective: To determine the characteristics that distinguish an 11-year series of non-Hodgkin lymphoma.
Methods: A descriptive study was carried out at Hermanos Ameijeiras Clinical Surgical Hospital from January 2011 to December 2021. The sample consisted of 132 patients diagnosed with the disease in the Hematology Service. A spectral and autocorrelation analysis was performed. Traditional and ARIMA models were used for the data. Forecasts were made with machine learning models.
Results: No trend was observed in the sequence graph and the repetition of the peaks suggests seasonality, which is demonstrated in the spectral analysis that occurs at 12 months. Through the additive analysis of the seasonal component, it was found that the highest positive seasonal factors belong to August (1.223) and December (0.969). The noise of the series has a normal distribution and machine learning algorithms reduce the forecast error.
Conclusions: The characteristics that distinguish a time series from a non-random time series of a stationary nature on average, with some variability of variances, with seasonality, where the error in forecast extrapolation decreases using machine learning algorithms, were determined.
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