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Diagnosing liver disease is the challenging task for many public health physicians. In this study, we propose the framework to diagnose the hepatitis disease. For this study the adaptive rule based induction were formulated and the adaptive rule implemented in combined Robust BoxCox Transformation (RBCT) and Neural Network (NN) methods. The performance of proposed model is compared and results are...
Analysis of a medical dataset having missing values and then filling the missing values through different approaches exists in the literature. However, the classification accuracies achieved using these approaches have not been so promising when analyzed. It is this reason; which implicitly motivated us to study and address new methods for imputation. In this paper, we propose an approach for efficient...
Pittsburgh compound B Positron Emission Tomography (PiB PET) imaging is a new technique to detect amyloid-beta (Aβ). Aβ is a pathological bio-data which appears distinctly in most neuro-degeneration diseases, such as Alzheimer's disease (AD). Although PiB PET imaging is relative mature, the accurate diagnosis of AD based on PiB PET images still remains a challenge for radiologists. To solve above...
Fluorodeoxyglucose Positron Emission Tomography — Computed Tomography (FDG PET-CT) is the preferred imaging modality for staging the lymphomas. Sites of disease usually appear as foci of increased FDG uptake. Thresholding is the most common method used to identify these regions. The thresholding method, however, is not able to separate sites of FDG excretion and physiological FDG uptake (sFEPU) from...
With the rapid maturity of internet and web technology over the last decades, the number of Indonesian online news articles is growing rapidly on the web at a pace we never experienced before. In this paper, we introduce a combination of rule-based and machine learning approach to find the sentences that have tropical disease information in them, such as the incidence date and the number of casualty,...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
The immune system in homo sapiens protects the body against diseases by identifying and attacking foreign pathogens. However, when the system misidentifies native cells as threats, it results in an auto-immune response. The auto-antibodies generated during this phenomenon may be identified through the indirect immunofluorescence test. An important constituent process of this test is the automated...
This paper presents an efficient Parkinson disease diagnosis system using Least Squares Twin Support Vector Machine (LSTSVM) and Particle Swarm Optimization (PSO). LSTSVM is a promising binary classifier and has shown better generalization ability and faster computational speed. PSO is used for feature selection and parameter optimization. Parkinson disease dataset is taken from UCI repository. The...
Data mining concepts have been extensively used for disease prediction in the medical field. Many Hybrid Prediction Models (HPM) have been proposed and implemented in this area, however, there is always a need for increasing accuracy and efficiency. The existing methods take into account all the features to build the classifier model thus reducing the accuracy and increasing the overall processing...
Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms and normal by the existing research. In this paper, the contributions are as two aspects: one is that the attributes of brain network of the resting state fMRI data have been calculated...
The aim of this study is to apply automatic speech recognition (ASR) mechanism to improve the amount of information extracted from the voice and to increase the accuracy of the system by using selective highly discriminative features among different types of acoustic features. For feature extraction, we applied three techniques which are Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction...
MicroRNAs (MiRNA) are small non-coding RNAs that regulate gene expression. Up to date, seventy miRNAs have been found differentially expressed in lung whole tissue between smoking patients affected by Chronic Obstructive Pulmonary Disease (COPD) and smokers. The aim of this study was to explore the associated miRNAs with emphysema severity of COPD. Firstly, we identified miRNAs differentially expressed...
Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different...
Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Physical inactivity is the leading cause of many diseases. It has been estimated that as many as 250,000 deaths per year in the United States, approximately 12% of the total, are attributable to a lack of regular physical activity. Measuring...
Prevalence of communicable and non-communicable diseases is one of the most important categories of epidemiological data that is used for interpreting health status of communities. This study is aimed to calculate the prevalence of outpatient diseases through characterization of outpatient prescriptions. The data used in this study is collected from 1412 prescriptions of various diseases and we have...
It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support...
To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia...
In this study a new dataset are prepared for neuromuscular diseases using scanning EMG method and four new features are extracted. These features are maximum amplitude, phase duration at the maximum amplitude, maximum amplitude times phase duration, and number of peaks. By using statistical values such as mean and variance, number of features has increased up to eight. This dataset was classified...
We propose a method to adaptively select an optimal cortical segmentation for brain connectivity analysis that maximizes feature-based disease classification performance. In standard structural connectivity analysis, the cortex is typically subdivided (parcellated) into N anatomical regions. White matter fiber pathways from tractography are used to compute an N ×N matrix, which represents the pairwise...
In medical field the diagnosis of heart disease is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the...
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