Search results for "Missing data"

A Multi-layer Feed Forward Neural Network Approach for Diagnosing Diabetes

2018

Diabetes is one of the worlds major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in an increase in serious complications such as heart attacks and deaths. Early diagnosis of diabetes, particularly of type 2 diabetes, is critical since it is vital for patients to get insulin treatments. However, diagnoses could be difficult especially in areas with few medical doctors. It is, therefore, a need for practical methods for the public for early detection and prevention with minimal intervention from medical professionals. A promising method for automated diagnosis is the use of artificial…

2013

Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequent…

Cost-description and multiple imputation of missing values: theSATisfaction and adherence to COPD treatment(SAT) study

2018

Aim:This article reports on a retrospective quarterly cost description (CD) performed on 401 patients with stable chronic obstructive pulmonary disease (COPD) at enrolment in the national, multicen...

Multivariate data analysis and bivariate regression studies applied to comparison of two multi-elemental methods for analysing wine samples

2002

Two inductively coupled plasma mass spectrometry (ICP-MS) methods which permit multi-elemental analysis in wine samples have been compared following two strategies. First, a multivariate tool based on principal component analysis (PCA) was employed for a global (all analytes) qualitative comparison of the two methods. A single plot based on the confidence limits of the Q and T2 PCA model statistics corresponding to the ‘standard’ method results (calibration set) was used to check the comparability of the ‘candidate’ method (test samples). The residual matrix (after test matrix interpolation into the PCA model) gives qualitative information about the nature of the main errors. This approach …

Missing Data

2009

In this chapter, we deal with the problem of missing data in principal component analysis (PCA) and partial least squares (PLS) methods. First, we review several statistical methods proposed in the literature for handling missing data. Both single and multiple imputation (MI) methods are studied and compared using simulated data. After this, we particularize the missing data problem for building and exploiting multivariate calibration models. Several approaches proposed in the literature are introduced and their performance compared based on several real data sets.

Seeing Missing Values

2011

Real-Time Human Pose Estimation from Body-Scanned Point Clouds

2015

International audience; This paper presents a novel approach to estimate the human pose from a body-scanned point cloud. To do so, a predefined skeleton model is first initialized according to both the skeleton base point and its torso limb obtained by Principal Component Analysis (PCA). Then, the body parts are iteratively clustered and the skeleton limb fitting is performed, based on Expectation Maximization (EM). The human pose is given by the location of each skeletal node in the fitted skeleton model. Experimental results show the ability of the method to estimate the human pose from multiple point cloud video sequences representing the external surface of a scanned human body; being r…

Listwise Recommendation Approach with Non-negative Matrix Factorization

2018

Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…

Missing values in deduplication of electronic patient data

2011

Data deduplication refers to the process in which records referring to the same real-world entities are detected in datasets such that duplicated records can be eliminated. The denotation ‘record linkage’ is used here for the same problem.1 A typical application is the deduplication of medical registry data.2 3 Medical registries are institutions that collect medical and personal data in a standardized and comprehensive way. The primary aims are the creation of a pool of patients eligible for clinical or epidemiological studies and the computation of certain indices such as the incidence in order to oversee the development of diseases. The latter task in particular requires a database in wh…

Metal artifact reduction in x-ray computed tomography: Inpainting versus missing value

2011

A comparison of algorithms for reduction of metal artifacts in x-ray cone beam computed tomography (CBCT) is presented. In the context of algebraic reconstruction techniques (ART) several inpainting algorithms in the image domain are evaluated against missing data strategies. A GPU-based iterative framework is employed for a meaningful comparison of both. Simulation results from an extended Shepp-Logan phantom and real world dental data are given.