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A Bayesian network classifier is one type of graphical probabilistic models that is capable of representing relationship between variables in a given domain under study. We consider the naïve Bayes, tree augmented naïve Bayes (TAN) and boosted augmented naïve Bayes (BAN) to classify patients with peptic ulcer disease among upper gastro intestinal bleeding patients. We compare their performance with...
Cancer is one of the terminal diseases humanity has to deal with today. It affects both old and young, male and female, all races and regions. In this paper, we implement a software system that monitors cancer incidences in Eastern Cape Province of South Africa. Using a Bayesian network model, the system predicts the likelihood of getting a particular type of Cancer taking into account the causal...
Polycystic Ovary Syndrome (PCOS) is one of the most common type of endocrine disorder in reproductive age women. This may result in infertility and anovulation. The diagnostic criterion includes the clinical and metabolic parameters which act as an early marker for the disease. We described a method that automates the PCOS detection based on these markers. Our algorithm involves the formulation of...
The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor...
A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This...
Dynamic Bayesian Belief networks (DBNs) have been commonly used to represent temporal data in several domains, however, an ideal representation requires a near perfect mapping between the process being modeled and the DBN. Furthermore, DBNs assume a full set of observations collected at a fixed frequency. Bayesian model selection has arisen to address biased inference and underlying assumptions about...
Statistics plays an important role in many areas especially in classification tasks. Logistic Regression Model is one popular technique to solve problems, in particular, medical problems. P-Thalassemia, a common genetic disorder, lends itself to is interesting for using MLR to classify types of P-Thalassemia. There are several types of Thalassemia in the world, especially Thailand. From many methods...
We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments...
This paper describes a methodology used to classify Alzheimer's disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ranges...
Abstract-We present a novel Bayesian network (BN) to classify strains of Mycobacterium tuberculosis complex (MTBC) into six major genetic lineages using mycobacterial interspersed repetitive units (MIRUs), a high-throughput biomarker. MTBC is the causative agent of tuberculosis (TB), which remains one of the leading causes of disease and morbidity world-wide. DNA fingerprinting methods such as MIRU...
Recently, Bayesian network classifiers (BNCs) have attracted many researchers because they can produce classification models with dependencies among attributes. From the application viewpoint, however, BNCs sometimes produce models too complicated to interpret easily. In this paper, we propose k-Bayesian network classifier (k-BNC), which is a new method to reconstruct the attribute-dependency relationship...
This paper proposes a neuro-rough model based on multi-layered perceptron (MLP) and rough set theory. The neuro-rough model is then tested on modeling the risk of HIV (human immunodeficiency virus) from demographic data. The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given...
Multi-dimensional classification is a generalization of supervised classification that considers more than one class variable to classify. In this paper we review the existing multi-dimensional Bayesian classifiers and introduce a new one: the KDB multi-dimensional classifier. Then we define different classification rules for multi-dimensional scope. Finally, we introduce a structural learning approach...
Many medical diagnosis applications are characterized by datasets that contain under- represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naive Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far...
With increasing studies in identifying pathology-induced group differences between patients and controls, there is also a growing need to simultaneously analyze multiple clinical measures, to elucidate group differences. In this paper, we present a novel learning-based method that uses Bayesian networks (BN) to model the inter-relationship between multiple clinical measures on facial expressions,...
This paper presents a new method to automatically grade pathological prostate images according to Gleason grading system. Two feature extraction methods were proposed based on fractal dimension to analyze the variations of intensity and texture complexity in images. Each image can be classified into appropriate grade by using Bayes classifier and k-Nearest-Neighbor (k-NN) classifier, respectively...
In this paper we present a Bayesian inference Multilayer Perceptron (MLP) which was used to classify the events of the Long Term ST Database (LTSTDB) as ischaemic or non-ischaemic episodes with an accuracy of 89.1%, sensitivity of 82.3% and specificity of 91.2% when the accuracy of the winning paper was 90.7%. The Automatic Relevance Determination (ARD) method was used to identify which of the extracted...
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