Assessing Capacity and Performance of Health Systems Using Principal Component Analysis: Results from Cross Sectional Survey in Kakamega County, Western Kenya

AbstractFor 41 species of Western Australian agamid lizards, we found that most appendage lengths vary isometrically, so shape is largely independent of size. Of the three methods we used to quantitatively remove the effects of size on shape, the two that use principal component analysis (PCA; Jolicoeur, 1963; Somers, 1986; 1989) provided similar results, whereas regression residuals (against body length) provided a different interpretation. Somers' size-free PCA approach to remove the size-effects was the most useful because it provided 'size-free' scores for each species that were further analysed using other techniques, and its results seemed more biologically meaningful. Some, but not all, of the variation in size-free shape for these lizards could be related to phylogeny, retreat choice and performance traits.

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Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.54.5.20 ◽

2019 ◽

Vol 54 (5) ◽

Author(s):

Hayder Ansaf ◽

Hayder Najm ◽

Jasim Mohammed Atiyah ◽

Oday A. Hassen

Keyword(s):

Principal Component Analysis ◽

Chaos Theory ◽

Principal Component ◽

Component Analysis ◽

Human Beings ◽

Multiple Parameters ◽

Detection Approach ◽

The Face ◽

And Performance ◽

Smile Detection

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.

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PENERAPAN ANALISIS KOMPONEN PRINSIPAL DALAM MEMBENTUK INDEKS PENGETAHUAN TENTANG FILARIASIS

Jurnal Ekologi Kesehatan ◽

10.22435/jek.17.3.613.146-154 ◽

2019 ◽

Vol 17 (3) ◽

pp. 146-154

Author(s):

Anton Suryatma ◽

Tities Puspita

Keyword(s):

Principal Component Analysis ◽

Predictor Variable ◽

Principal Component ◽

Component Analysis ◽

Cross Sectional Study ◽

Cross Sectional ◽

Future Studies ◽

Multicentre Research ◽

Health Research And Development ◽

Structured Questionnaire

ABSTRACT Knowledge about filariasis is one of many importance dimensions of succsessing filariasis elimination in Indonesia. This study aims at forming knowledge index about filariasis using principal component analysis. Principal component analysis methods have been used to reduce the researcher subjectivity in making knowledge composit. Data was from multicentre research on filaria 2017 by Indonesian National Institute of Health Research and Development. It was a cross sectional study conducted in 23 districts with 13,266 respondents. Ten questions about the causes and impacts of filariasis were asked with a structured questionnaire. Tetrachoric correlation and principal component analysis were used in data analysis. The knowledge index could explain 45.18% (rho=0.4518) of knowledge variations from the ten questions. This index can potentially be used as an output or a predictor variable in advance analysis. Future studies should take into account all levels and depths of knowledge when forming a knowledge composit. Keywords: knowledge, filaria, filariasis, principal component analysis ABSTRAK Pengetahuan tentang filariasis merupakan salah satu dimensi penting dalam keberhasilan eliminasi filariasis di Indonesia. Studi ini bertujuan untuk membentuk indeks pengetahuan tentang filariasis menggunakan metode analisis komponen prinsipal. Metode analisis komponen prinsipal digunakan untuk mengurangi subjektifitas peneliti dalam membentuk komposit pengetahuan. Data yang digunakan berasal dari penelitian multisenter filariasis 2017 yang dilakukan oleh Badan Penelitian dan Pengembangan Kesehatan. Penelitian tersebut merupakan penelitian potong lintang di 23 Kabupaten dengan 13.266 responden. Terdapat sepuluh pertanyaan yang ditanyakan melalui kuesioner terstruktur mengenai penyebab dan akibat dari filariasis. Data dianalisis dengan korelasi tetrakorik dan analisis komponen prinsipal. Indeks pengetahuan filariasis yang terbentuk dapat menjelaskan 45,18% (rho=0,4518) variasi pengetahuan dari 10 pertanyaan. Indeks ini dapat digunakan dalam analisis lanjutan sebagai variabel output atau prediktor. Disarankan untuk mempertimbangkan tingkatan dan kedalaman pengetahuan apabila hendak membentuk komposit pengetahuan filariasis. Kata kunci: pengetahuan, filaria, filariasis, analisis komponen prinsipal

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Sociodemographic characteristics and dietary patterns in cardiometabolic risk subjects

British Food Journal ◽

10.1108/bfj-04-2019-0259 ◽

2019 ◽

Vol 121 (11) ◽

pp. 2780-2790 ◽

Cited By ~ 1

Author(s):

Brenda Kelly Souza Silveira ◽

Juliana Farias de Novaes ◽

Sarah Aparecida Vieira ◽

Daniela Mayumi Usuda Prado Rocha ◽

Arieta Carla Gualandi Leal ◽

...

Keyword(s):

Principal Component Analysis ◽

Dietary Patterns ◽

Cardiovascular Health ◽

Cardiometabolic Risk ◽

Principal Component ◽

Component Analysis ◽

Unhealthy Lifestyle ◽

Cross Sectional ◽

Content Type ◽

Traditional Pattern

Purpose The purpose of this paper is to examine the associations of dietary patterns with sociodemographic and lifestyle characteristics in a cardiometabolic risk population. Design/methodology/approach In this cross-sectional study data from 295 (n=123 men/172 women, 42±16 years) participants in a Cardiovascular Health Care Program were included. After a 24-hour recall interview the dietary patterns were determined using principal component analysis. Sociodemographic, clinical and lifestyle data were collected by medical records. Findings Subjects with diabetes and hypertension had a higher adherence in the “traditional” pattern (rice, beans, tubers, oils and meats). Poisson regression models showed that male subjects with low schooling and smokers had greater adherence to the “traditional” pattern. Also, students, women, and those with higher schooling and sleeping =7 h/night showed higher adherence to healthy patterns (whole grains, nuts, fruits and dairy). Women, young adults and those with higher schooling and fewer sleep hours had greater adherence to healthy dietary patterns. Those with low schooling and unhealthy lifestyle showed more adherence to the “traditional” pattern. Social implications The results indicate the importance to personalized nutritional therapy and education against cardiometabolic risk, considering the dietary patterns specific to each population. Originality/value Socioeconomic and lifestyle characteristics can influence dietary patterns and this is one of the few studies that investigated this relationship performing principal component analysis.

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Tarsal Morphology Differs in Plano Valgus Feet

Foot & Ankle Orthopaedics ◽

10.1177/2473011419s00057 ◽

2019 ◽

Vol 4 (4) ◽

pp. 2473011419S0005

Author(s):

Erik S. Moore ◽

Matthew W. Kindig ◽

Daniel A McKearney ◽

Scott Telfer ◽

Bruce Sangeorzan ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Statistical Modelling ◽

Component Analysis ◽

Published Data ◽

Flat Foot ◽

Cross Sectional ◽

Men And Women ◽

Medial Side ◽

History Of

Category: Hindfoot Introduction/Purpose: While there are established associated conditions, the intrinsic cause of symptomatic adult flatfoot is not known. There are published data suggesting that the relationship of the hindfoot bones in acquired flatfoot are subluxated. And there is some support in 2 D for the concept that the bones are shaped differently in flatfoot but the complexity of bone shape and human variation makes comparisons difficult. The purpose of this study was to utilize principal component analysis (PCA) to determine whether morphology of the hind- and midfoot bones differs in neutral and plano-valgus feet. Methods: Forty subjects (23 male and 17 Female, average age 52.6 +/- 8.9) with no history of injury or surgery underwent bilateral foot WB CT scan for another study. The talus, calcaneus, navicular and cuboid were segmented into bone models from these previously acquired CT images. Morphometric Shape analysis software (Geomorph) was used to assess shape variations among foot types using Principal component analysis (PCA). PCA is a statistical modelling technique used to study variation in the shape of structures that are difficult to compare and bring out strong patterns in a dataset objectively. Forty feet had been classified into 4 foot groups prior to this study; neutral, cavus, asymptomatic flatfoot and symptomatic flatfoot. This study included the painful flatfeet and neutral feet. Each bone was compared between the feet that were flat and those that were neutral. Comparisons were made between men and women as well. Results: There were no differences between groups in age or sex. There were 38 principle components identified. The first two PC accounted for 38% of the calcaneal variation and 33% of the talar variation. Subtle differences between men and women were found only at the talus and navicular. The cuboid did not exhibit any differences between foot types. The navicular in symptomatic planus had a more posteriorly positioned tuberosity (tuber wrapped around the medial side of the talus) and were wider than neutral feet. The calcaneus showed planus feet to have calcanei that have decreased height and increased length compared to neutrally aligned feet. The cross -sectional area of the calcaneus was reduced compared to neutral feet. The talar shape was not different in the PC. Conclusion: A flat foot is present in over 20% of the population and most often not symptomatic. The root cause of symptomatic adult plano valgus foot is not known and is likely multi factorial. The study demonstrates that there is intrinsic difference in the shape of the calcaneus and navicular bones in flat foot. it is possible that the catalyst for collapse is bony rather than soft tissue.

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Landmark-Based Shape Analysis on Middle Cerebral Intracranial Aneurysms: A Geometric Morphometrics Approach to Infer Natural History

Current Neurovascular Research ◽

10.2174/1567202617999201214230944 ◽

2020 ◽

Vol 17 ◽

Author(s):

Vincent M. Tutino ◽

Anthony J. Yan ◽

Sricharan S. Veeturi ◽

Tatsat R. Patel ◽

Hamidreza Rajabzadeh-Oghaz ◽

...

Keyword(s):

Principal Component Analysis ◽

Natural History ◽

Geometric Morphometrics ◽

Hierarchical Clustering ◽

Intracranial Aneurysms ◽

Shape Change ◽

Principal Component ◽

Component Analysis ◽

Maximum Height ◽

Cross Sectional

Background:: Due to scarcity of longitudinal data, the morphologic development of intracranial aneurysms (IAs) during their natural history remains poorly understood. However, longitudinal information can often be inferred from cross-sectional datasets as demonstrated by anatomists’ use of geometric morphometrics to build evolutionary trees, reconstructing species inter-relationships based on morphologic landmarks. Objective:: We adopted these tools to analyze cross-sectional image data and infer relationships between IA morphologies. Methods:: On 3D reconstructions of 52 middle cerebral artery (MCA) IAs (9 ruptured) and 10 IA-free MCAs (baseline geometries), 7 semi-automated landmarks were placed at the proximal parent artery and maximum height. From these, 64 additional landmarks were computationally generated to create a 71-landmark point cloud of 213 xyz coordinates. This data was normalized by Procrustes transformation and used in principal component analysis, hierarchical clustering, and phylogenetic analyses. Results:: Principal component analysis showed separation of IA-free MCA geometries and grouping of ruptured IAs from unruptured IAs. Hierarchical clustering delineated a cluster of only unruptured IAs that were significantly smaller and more spherical than clusters that had ruptured IAs. Phylogenetic classification placed ruptured IAs more distally in the tree than unruptured IAs, indicating greater shape derivation. Groups of unruptured IAs were observed, but ruptured IAs were invariably found in mixed lineages with unruptured IAs, suggesting that some pathways of shape change may be benign while other are more associated with rupture. Conclusion:: Geometric morphometric analyses of larger datasets may indicate particular pathways of shape change leading toward aneurysm rupture versus stabilization.

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Application of Benchmarking and Principal Component Analysis in Measuring Performance of Public Irrigation Schemes in Kenya

Agriculture ◽

10.3390/agriculture8100162 ◽

2018 ◽

Vol 8 (10) ◽

pp. 162 ◽

Cited By ~ 3

Author(s):

Faith Muema ◽

Patrick Home ◽

James Raude

Keyword(s):

Principal Component Analysis ◽

Performance Indicators ◽

Principal Component ◽

Component Analysis ◽

Performance Score ◽

Irrigation Scheme ◽

Western Kenya ◽

Average Performance ◽

Management Measures ◽

Linear Aggregation

The inefficient water use, and variable and low productivity in Kenyan public irrigation schemes is a major concern. It is, therefore, necessary to periodically monitor and evaluate the performance of public irrigation schemes. This prompted evaluation of performance of three rice growing irrigation schemes in western Kenya using benchmarking and principal component analysis. The aim of the study was to quantify and rank the performance of selected irrigation schemes. The performance of the irrigation schemes was evaluated for the period from 2012 to 2016 using eleven performance indicators under agricultural productivity, water supply and financial performance categories. The performance indicators were weighted using principal component analysis and combined to form a single performance score using linear aggregation method. The average performance in the Ahero, West Kano and Bunyala irrigation schemes was 48%, 49% and 56%, respectively. Based on performance score, the Bunyala irrigation scheme is the highest performing rice irrigation scheme in western Kenya. The three irrigation schemes have an average performance. Operation and management measures to improve the current performance of the irrigation schemes are needed.

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Characterising 3D spherical packings by principal component analysis

Engineering Computations ◽

10.1108/ec-05-2019-0225 ◽

2019 ◽

Vol 37 (3) ◽

pp. 1023-1041 ◽

Cited By ~ 1

Author(s):

Tingting Zhao ◽

Y.T. Feng ◽

Yuanqiang Tan

Keyword(s):

Principal Component Analysis ◽

Gaussian Quadrature ◽

Three Dimensional ◽

Principal Component ◽

Component Analysis ◽

Particle Packing ◽

Cross Sectional ◽

Content Type ◽

Computer Methods ◽

Novel Approach

Purpose The purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional. Design/methodology/approach Gaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images). Findings Differences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach. Originality/value Develop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences.

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