A decision tree--based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

Categorizing SHR and WKY rats by chi2 algorithm and decision tree

Classifying mental disorder is a big issue in psychology in recent years. This article focuses on offering a relation between decision tree and encoding of fMRI that can simplify the analysis of different mental disorders and has a high ROC over 0.9. Here we encode fMRI information to the power-law distribution with integer elements by the graph theory in which the network is characterized by degrees that measure the number of effective links exceeding the threshold of Pearson correlation among voxels. When the degrees are ranked from low to high, the network equation can be fit by the power-law distribution. Here we use the mentally disordered SHR and WKY rats as samples and employ decision tree from chi2 algorithm to classify different states of mental disorder. This method not only provides the decision tree and encoding, but also enables the construction of a transformation matrix that is capable of connecting different metal disorders. Although the latter attempt is still in its fancy, it may have a contribution to unraveling the mystery of psychological processes.

Differentiating Post-Polio Syndrome from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome

Background

Overlapping and concomitant symptoms among similar chronic illnesses have created difficulties for diagnosis and further treatment. Three such chronically fatiguing illnesses, Post-polio syndrome (PPS), Myalgic Encephalomyelitis (ME) and chronic fatigue syndrome (CFS) fall under this category.

Purpose

The aim of this study is to examine and distinguish between core symptoms found in these illnesses (i.e. muscle pain/weakness, fatigue or exhaustion, and autonomic symptoms) via three methods of analysis (DePaul Symptom Questionnaire 2 (DSQ-2), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and machine learning techniques).

Results

Items assessing onset and severity for individuals who reported having PPS were found to have experienced an onset of PPS related symptoms roughly 30 years after the onset of Polio. Items found in the DSQ-2, SF-36 compared all illness groups and found that participants with ME/CFS were more functionally impaired across symptoms than those with PPS. Across all analyses, three domains most commonly differentiated the illnesses (neurocognitive, Post-exertional malaise, and neuroendocrine).

Conclusion

Examining functional impairment amongst chronically fatiguing illnesses using multiple methods of analysis can be an important factor in distinguishing similar illnesses. These findings support further analysis of analogous symptomatology among other chronic illnesses to assist in diagnosis.

Computerized Systems Supporting Clinical Decision in Medicine

Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems.

Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application.

Temporally-aware algorithms for the classification of anuran sounds

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts.

Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques

The objectives of this study are to assess various automated texture features obtained from the segmented colony regions of induced pluripotent stem cells (iPSCs) and confirm their potential for characterizing the colonies using different machine learning techniques. One hundred and fifty-one features quantified using shape-based, moment-based, statistical and spectral texture feature groups are extracted from phase-contrast microscopic colony images of iPSCs. The forward stepwise regression model is implemented to select the most appropriate features required for categorizing the colonies. Support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and adaptive boosting (Adaboost) classifiers are used with ten-fold cross-validation to evaluate the texture features within each texture feature group and fused-features group to characterize healthy and unhealthy colonies of iPSCs. Overall, based on the classification performances of the four texture feature groups using the five classifier models, statistical features always exhibit a high predictive capacity (>87.5%). However, the classification performance using fused texture patterns with statistical, shape-based, and moment-based features was found to be robust and reliable with fewer false positive and false negative values compared to the features when either one is used for the classification of colonies of iPSCs. Furthermore, the results showcase that the SVM, RF and Adaboost classifiers deliver better classification performances than DT and MLP. Our findings suggest that the proposed automated fused statistical, shape-based, and moment-based texture pattern features trained with machine learning techniques are potentially more appropriate and helpful to biologists for characterizing colonies of stem cells.

Paediatric heart sound signal analysis towards classification using multifractal spectra

Healthy versus unhealthy heart sound computer-aided classification tools are very popular for supporting clinical decisions. In this paper a new method is proposed for the classification of heart sound recordings from a statistical standpoint without detection and localization of fundamental heart sounds (S1, S2). This study analyzes the possibility of detecting healthy heart sound signal from a large set of measurements, corresponding to different pathologies, such as aortic regurgitation, mitral regurgitation, aortic stenosis and ventricular septal defects. The proposed method employs singularity spectra analysis and long-term dependency of irregular structures. Healthy signals are firstly separated from the rest of the recordings. In the second step, the signals with a click syndrome, used here as a reference, are detected in the unhealthy group. Innocent murmurs have not been considered in this paper. Each auscultatory recording is classified into one of the following classes: healthy; click syndrome; and other heart dysfunctions. The results of the proposed method provided high recall and precision values for each of the three classes. Since the presence of additive noise may affect the classification, we also analyzed the possibility of classifying signals in such circumstances. The method was tested, verified and showed high accuracy.

Evaluating a decision making system for cardiovascular dysautonomias diagnosis

Autonomic nervous system (ANS) is the part of the nervous system that is involved in homeostasis of the whole body functions. A malfunction in this system can lead to a cardiovascular dysautonomias. Hence, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. The purpose of this study is to develop and evaluate a decision tree based cardiovascular dysautonomias prediction system on a dataset collected from the ANS unit of the Moroccan university hospital Avicenne. We collected a dataset of 263 records from the ANS unit of the Avicenne hospital. This dataset was split into three subsets: training set (123 records), test set (55 records) and validation set (85 records). C4.5 decision tree algorithm was used in this study to develop the prediction system. Moreover, Java Enterprise Edition platform was used to implement a prototype of the developed system which was deployed in the Avicenne ANS unit so as to be clinically validated. The performance of the decision tree-based prediction system was evaluated by means of the error rate criterion. The error rates were measured for each classifier and have achieved an average value of 1.46, 2.24 and 0.89 % in training, test, and validation sets respectively. The results obtained were encouraging but further replicated studies are still needed to be performed in order to confirm the findings of this study.

Current trends and perspectives for automated screening of cardiac murmurs

Although in high income countries rheumatic heart disease is now rare, it remains a major burden in low and middle income countries. In these world areas, physicians and expert sonographers are rare, and screening campaigns are usually performed by nomadic caregivers who can only recognise patients in an advanced phase of heart failure with high economic and social costs. Therefore, great interest exists regarding the possibility of developing a simple, low-cost procedure for screening valvular heart disease. With the development of computer science, the cardiac sound signal can be analysed in an automatic way. More precisely, a panel of features characterising the acoustic signal are extracted and sent to a decision-making software able to provide the final diagnosis. Although no system is currently available in the market, the rapid evolution of these technologies recently led to the activation of clinical trials. The aim of this note is to review the state of advancement of this technology (trends in feature selection and automatic diagnostic strategies), data available regarding performance of the technology in the clinical setting and finally what obstacles still need to be overcome before automated systems can be clinically/commercially viable.

Toward automatic detection of vessel stenoses in cerebral 3D DSA volumes

Vessel diseases are a very common reason for permanent organ damage, disability and death. This fact necessitates further research for extracting meaningful and reliable medical information from the 3D DSA volumes. Murray's law states that at each branch point of a lumen-based system, the sum of the minor branch diameters each raised to the power x, is equal to the main branch diameter raised to the power x. The principle of minimum work and other factors like the vessel type, impose typical values for the junction exponent x. Therefore, deviations from these typical values may signal pathological cases. In this paper, we state the necessary and the sufficient conditions for the existence and the uniqueness of the solution for x. The second contribution is a scale- and orientation- independent set of features for stenosis classification. A support vector machine classifier was trained in the space of these features. Only one branch was misclassified in a cross validation on 23 branches. The two contributions fit into a pipeline for the automatic detection of the cerebral vessel stenoses.

Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees

BACKGROUND

At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques.

OBJECTIVE

The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system.

DESIGN

Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals.

RESULTS

This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively).

CONCLUSIONS

ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.

A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software

BACKGROUND

Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses.

MATERIALS AND METHODS

Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes.

RESULTS

Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree.

CONCLUSION

BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources.

Developments in ambulatory electrocardiography

Cardiovascular disease (CVD) is the leading cause of death in many developed countries. There is a need for cardiovascular monitoring devices that can supplement traditional medical and clinical care by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that are precursors to sudden cardiac death, and the long-term management of chronic conditions through monitoring of symptoms, risk factors, and the effects of therapy. This paper provides an overview of ambulatory electrocardiogram (ECG) monitors, which are one of the most widely prescribed diagnostic procedures for cardiovascular disease. The engineering challenges associated with ambulatory electrocardiography are discussed, and technological improvement areas that are the focus of current research effort are reviewed.

Feature extraction for systolic heart murmur classification

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an "intelligent stethoscope" with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil's sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Diagnosing aortic valve stenosis by parameter extraction of heart sound signals

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.

Diagnosing aortic valve stenosis by correlation analysis of wavelet filtered heart sounds

Traditional auscultation performed by the general practitioner remains problematic and often gives significant results only in a late stage of heart valve disease. Valve stenoses and insufficiencies are nowadays diagnosed with accurate but expensive ultrasonic devices. This study aimed to develop a new heart sound analysis method for diagnosing aortic valve stenoses (AVS) based on a wavelet and correlation technique approach. Heart sounds recorded from 373 patients (107 AVS patients, 61 healthy controls (REF) and 205 patients with other valve diseases (OVD)) with an electronic stethoscope were wavelet filtered, and envelopes were calculated. Three correlations on the basis of these envelopes were performed: within the AVS group, between the groups AVS and REF and between the groups AVS and OVD, resulting in the mean correlation coefficients rAVS, rAVSv.REF and rAVSv.OVD. These results showed that rAVS (0.783 +/- 0.097) is significantly higher (p < 0.0001) than rAVSv.REF (0.590 +/- 0.056) and rAVSv.OVD (0.516 +/- 0.056), leading to a highly significant discrimination between the groups. The wavelet and correlation-based heart sound analysis system should be useful to general practitioners for low-cost, easy-to-use automatic diagnosis of aortic valve stenoses.