A functional supervised learning approach to the study of blood pressure data

Papayiannis, Georgios I.; Giakoumakis, E. A.; Moulopoulos, Spyros D.; Stamatelopoulos, Kimon; Toumanidis, Savvas; Yannacopoulos, A. N.

doi:10.1002/sim.7587

Cited by 4 publications

(5 citation statements)

References 25 publications

(39 reference statements)

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“…The pattern from the FPCA gives a direct biological interpretation and offers a visual tool to assess the main directions in the functional data. The FPCA approach were shown to be providing a better estimate compared to other conventional methods to handle longitudinal data in biomedical applications [ 1 , 2 , 9 , 10 , 21 , 35 ] and characterise trajectories in order to classify the pattern in child growth study [ 9 ] and various field of studies [ 12 , 22 , 23 , 28 , 33 – 35 , 37 – 39 ].…”

Section: Discussionmentioning

confidence: 99%

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Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Karuppusami

Antonisamy

Premkumar

2022

BMC Med Res Methodol

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Background Longitudinal studies are important to understand patterns of growth in children and limited in India. It is important to identify an approach for characterising growth trajectories to distinguish between children who have healthy growth and those growth is poor. Many statistical approaches are available to assess the longitudinal growth data and which are difficult to recognize the pattern. In this research study, we employed functional principal component analysis (FPCA) as a statistical method to find the pattern of growth data. The purpose of this study is to describe the longitudinal child growth trajectory pattern under 3 years of age using functional principal component method. Methods Children born between March 2002 and August 2003 (n = 290) were followed until their third birthday in three neighbouring slums in Vellore, South India. Field workers visited homes to collect details of morbidity twice a week. Height and weight were measured monthly from 1 month of age in a study-run clinic. Longitudinal child growth trajectory pattern were extracted using Functional Principal Component analysis using B-spline basis functions with smoothing parameters. Functional linear model was used to assess the factors association with the growth functions. Results We have obtained four FPCs explained by 86.5, 3.9, 3.1 and 2.2% of the variation respectively for the height functions. For height, 38% of the children’s had poor growth trajectories. Similarly, three FPCs explained 76.2, 8.8, and 4.7% respectively for the weight functions and 44% of the children’s had poor growth in their weight trajectories. Results show that gender, socio-economic status, parent’s education, breast feeding, and gravida are associated and, influence the growth pattern in children. Conclusions The FPC approach deals with subjects’ dynamics of growth and not with specific values at given times. FPC could be a better alternate approach for both dimension reduction and pattern detection. FPC may be used to offer greater insight for classification.

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Section: Discussionmentioning

confidence: 99%

“…The phase variation deals with the differences in timing of important features between the functions. The registration technique was carried out to improve the curve misalignment [ 12 , 23 , 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Karuppusami

Antonisamy

Premkumar

2022

BMC Med Res Methodol

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“…Such objects do not necessarily belong to a vector space, since summing the two sampled objects does not necessarily lead to an object of the underlying space. This type of statistical modelling has found several applications in practice so far, e.g., in image and signal processing [26,27], in analyzing point processes [28], in medicine [29], in electric energy prediction [30], etc. Restricting attention to the case where f : I → R is a function from some interval of I (I = [0, 1] without loss of generality) is a suitable choice for the study of nonlinear profiles.…”

Section: The Framework Of Deformation Modelsmentioning

confidence: 99%

Modelling of Functional Profiles and Explainable Shape Shifts Detection: An Approach Combining the Notion of the Fréchet Mean with the Shape-Invariant Model

Papayiannis,

Psarakis,

Yannacopoulos

2023

Mathematics

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A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape-invariant model, allows for interpretable parameterizations of the profile’s deviations from the typical shape. The EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process are further distinguished into significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modeling and shift detection framework is implemented to a real-world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modeled, while profiles indicating hazardous concentration levels are successfully identified in most cases.

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Section: Smoothing Outlying Function and Fpcamentioning

confidence: 99%

Identifying longitudinal child growth curve pattern using Functional Principal Component Analysis from birth cohort study in South India

Karuppusami¹,

Antonisamy²,

Premkumar³

2021

Preprint

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Background: Human growth studies has long been of interest to researchers and health authorities. Predominantly, the study of physical growth in children is a challenging and complex issue. The goal of a variety of studies from across the world is to develop overall health and well-being in children. It is therefore important that we need to identify an accurate and reliable approach for characterising growth trajectories to distinguish between children who have healthy growth and those growth is poor. Many statistical approaches are available to assess the longitudinal growth data and which are difficult to recognize the pattern. The purpose of this study is to identify the longitudinal child growth trajectory pattern and factors association on the growth function using an advanced statistical technique. Methods: This longitudinal birth cohort study (n=290) was conducted in three nearby urban slums communities in Vellore, South India. Pregnant women were identified during a survey conducted in 2002 and infants were recruited from birth between the period of March 2002 and August 2003 following written informed consent from the mother. Growth outcomes of height and weight measurements were recorded for each child continuously in first 36 months. Functional Principal Component Analysis was used to classify the longitudinal child growth trajectory pattern. Functional linear model was used to assess the factors association with the growth functions. Results: We have obtained four functional principal components explained by 86.5%, 3.9%, 3.1% and 2.2% of the variation respectively for the height functions. 38% of the children’s had poor growth trajectories in height. Similarly, we have obtained three functional principal components explained 76.2%, 8.8%, and 4.7% respectively for the weight functions. 44% of the children’s had poor growth in their weight trajectories. The study shows that gender, socio-economic status, parent’s education, breast feeding, and gravida are associated and, influence the growth pattern in children. Conclusions: The advanced FPC approach deals with subjects’ dynamics of growth and not with specific values at given times. FPCA may be a better alternative approach in the sense of both dimension reduction and pattern detection. FPCA may be used to offer greater insight for classification.

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A functional supervised learning approach to the study of blood pressure data

Cited by 4 publications

References 25 publications

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Modelling of Functional Profiles and Explainable Shape Shifts Detection: An Approach Combining the Notion of the Fréchet Mean with the Shape-Invariant Model

Identifying longitudinal child growth curve pattern using Functional Principal Component Analysis from birth cohort study in South India

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