[Prognosis of the therapeutic results of radiotherapeutic procedures using a fuzzy classification algorithm].
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Humans↗
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Results for “Classification Algorithms”
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[Comment on the paper: "A simplified classification algorithm for overall chest views of surgical lung patients" by R. Herlbauer].
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Adult↗
An arrhythmia classification system based on the RR-interval signal.
OBJECTIVE: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. METHODOLOGY: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2 degrees heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2 degrees heart block. RESULTS: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. CONCLUSION: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.
Algorithms↗
Three-dimensional ultrasound analyses of the prostate.
Although conventional ultrasonography has proven to be clinically useful for depicting many types of cancerous lesions, it cannot distinguish reliably between cancerous and noncancerous tissue of the prostate. Therefore, conventional transrectal ultrasonography (TRUS) is used primarily for general evaluations of the gland and for guiding biopsies based on clearly imaged anatomic features such as the capsule, seminal vesicles, and urethra. Spectrum analysis extracts ultrasound signal parameters associated with biopsy-proven tissue types, and these parameters are then classified using neural network tools such as learning vector quantization, radial basis, and multilayer perceptron algorithms. Classification of cancerous and noncancerous prostate tissue using neural networks produces receiver operating characteristic (ROC) curves of 0.87 +/- 0.04 compared with 0.64 +/- 0.04 for conventional ultrasonography. To image the prostate using these methods, parameter values are computed at each pixel location, then translated into a score for the likelihood of cancer using a look-up table generated using the best classification algorithm. The score for cancer likelihood is expressed as a gray-scale or color value, and the resulting image may be useful to guide biopsies or therapy. Changes in parameter or score values over time potentially can be used to assess progression of disease or efficacy of therapy.
Adenocarcinoma↗
Combined 5 x 2 cv F test for comparing supervised classification learning algorithms.
Dietterich (1998) reviews five statistical tests and proposes the 5 x 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 x 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 x 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 x 2 cv proper.
Algorithms↗
Classification of gait patterns in spastic hemiplegia and spastic diplegia: a basis for a management algorithm.
Classifications of gait and postural patterns in spastic hemiplegia and spastic diplegiía are presented, based on the work of previous authors. The classifications are used as a biomechanical basis, linking spasticity, musculoskeletal pathology in the lower limbs, and the appropriate intervention strategies. The choice of target muscles for spasticity management, the muscle contractures requiring lengthening and the choice of orthotics are then linked to the underlying gait pattern.
Algorithms↗
Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm.
Classification of homologous chromosomes is essential to advanced studies of cancer genetics. Centromere intensities are believed to be an important differentiating feature between homologs. Therefore, segmentation of centromeres is a major step toward the realization of homolog classification. This paper describes an iterative fuzzy algorithm which successfully segments centromeres from images of human chromosomes prepared using fluorescence in-situ hybridization technique. The algorithm is based on assigning a fuzzy membership value to each pixel in the centromere image. An iterative algorithm then updates and minimizes a defined error function. Chromosome 22, a highly heteromorphic chromosome, is used to verify the centromere segmentation method. Homologs of this chromosome are classified based on their segmented centromere intensities as well as their morphological differences. The classification results of these two methods agree completely and are used to validate our developed algorithm.
Algorithms↗
Can the US minimum data set be used for predicting admissions to acute care facilities?
This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value.
Algorithms↗
Automatic Paroxysmal Atrial Fibrillation Based on not Fibrillating ECGs.
OBJECTIVES: The objective of the paper is to describe an automatic algorithm for Paroxysmal Atrial Fibrillation (PAF) Detection, based on parameters extracted from ECG traces with no atrial fibrillation episode. The modular automatic classification algorithm for PAF diagnosis is developed and evaluated with different parameter configurations. METHODS: The database used in this study was provided by Physiobank for The Computers in Cardiology Challenge 2001. Each ECG file in this database was translated into a 48 parameter vector. The modular classification algorithm used for PAF diagnosis was based on the nearest K-neighbours. Several configuration options were evaluated to optimize the classification performance. RESULTS: Different configurations of the proposed modular classification algorithm were tested. The uni-parametric approach achieved a top classification rate value of 76%. A multi-parametric approach was configured using the 5 parameters with highest discrimination power, and a top classification rate of 80% was achieved; different functions to typify the parameters were tested. Finally, two automatic parametric scanning strategies, Forward and Backward methods, were adopted. The results obtained with these approaches achieved a top classification rate of 92%. CONCLUSIONS: A modular classification algorithm based on the nearest K-neighbours was designed. The classification performance of the algorithm was evaluated using different parameter configurations, typification functions and number of K-neighbors. The automatic parametric scanning techniques achieved much better results than previously tested configurations.
Algorithms↗
The interrater reliability among physical therapists newly trained in a classification system for acute low back pain.
STUDY DESIGN: A prospective methodological interrater reliability study. OBJECTIVES: To calculate the interrater reliability among clinicians newly trained in a classification system for acute low back pain and to determine the level of agreement at key junctures within the classification algorithm. BACKGROUND: The utility of a classification system for patients with low back pain depends on its reliability and generalizability. To be practical, clinicians must be able to apply the system after a reasonable amount of training. Identifying key points in the classification algorithm where disagreement occurs can lead to better operational definitions. METHODS: Four physical therapists read an article and attended a 1-day training session in the classification system. Randomly paired therapists classified patients referred for treatment of acute low back pain and noted decisions at key junctures in the system algorithm. RESULTS: Forty-five patients were classified. Repeated examinations did not increase the patient's pain (P>.05). For 3 out of the 4 therapists, the interrater reliability showed a kappa value of 0.45. The fourth therapist, excluded from the overall analysis, exhibited a bias towards the immobilization classification. Among the 3 therapists, major disagreement occurred with the determination of symmetry with trunk side bending and the effects of repeated movements. CONCLUSIONS: Three out of 4 clinicians newly trained in the system showed moderate reliability. The reliability was slight when the fourth therapist was included. Refinement of the operational definitions and criteria for determining lumbar capsular patterns are needed. One day of training is probably not adequate for all therapists, especially for those biased towards specific low back pain syndromes.
Adult↗
Assessing the accuracy of prediction algorithms for classification: an overview.
We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.
Algorithms↗
Utility of the blood for gene expression profiling and biomarker discovery in chronic fatigue syndrome.
Chronic fatigue syndrome (CFS) is a debilitating illness lacking consistent anatomic lesions and eluding conventional laboratory diagnosis. Demonstration of the utility of the blood for gene expression profiling and biomarker discovery would have implications into the pathophysiology of CFS. The objective of this study was to determine if gene expression profiles of peripheral blood mononuclear cells (PMBCs) could distinguish between subjects with CFS and healthy controls. Total RNA from PBMCs of five CFS cases and seventeen controls was labeled and hybridized to 1764 genes on filter arrays. Gene intensity values were analyzed by various classification algorithms and nonparametric statistical methods. The classification algorithms grouped the majority of the CFS cases together, and distinguished them from the healthy controls. Eight genes were differentially expressed in both an age-matched case-control analysis and when comparing all CFS cases to all controls. Several of the differentially expressed genes are associated with immunologic functions (e.g., CMRF35 antigen, IL-8, HD protein) and implicate immune dysfunction in the pathophysiology of CFS. These results successfully demonstrate the utility of the blood for gene expression profiling to distinguish subjects with CFS from healthy controls and for identifying genes that could serve as CFS biomarkers.
Adult↗
A noninvasive technique for detecting hypernasal speech using a nonlinear operator.
Speakers with a defective velopharyngeal mechanism produce speech with inappropriate nasal resonance (hypernasal speech). It is of clinical interest to detect hypernasality as it is indicative of an anatomical, neurological, or peripheral nervous system problem. There are various clinical techniques used to determine hypernasality. The current techniques are physically invasive or intrusive to some extent. A preferred approach for detecting hypernasality, would be noninvasive to maximize patient comfort and naturalness of speaking. In this study, a noninvasive technique based on the Teager Energy operator is proposed. Utilizing a property of the Teager Energy operator and a model for normal and nasalized speech, a significant difference between the Teager Energy profile for lowpass and bandpass filtered nasalized speech is shown. This difference is shown to be nonexistent for normal speech. A classification algorithm is formulated that detects the presence of hypernasality using a measure of the difference in the Teager Energy profiles. The classification algorithm was evaluated using a native English speaker population producing front (/i/) and mid (/A/) vowels. Results show that the presence of hypernasality in speech can be reliably detected using the proposed classification algorithm.
Algorithms↗
Optical detection of high-grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study.
OBJECTIVE: The purpose of this study was to assess the in vivo optical detection of high-grade cervical intraepithelial neoplasia (2/3+) on the whole cervix with a noncontact, spectroscopic device. STUDY DESIGN: Cervical scanning devices collected intrinsic fluorescence and broadband white light spectra and video images from 604 women during routine colposcopy examinations at 6 clinical centers. A statistically significant dataset was developed of intrinsic fluorescence and white light-induced cervical tissue spectra that was correlated to expert histopathologic determination. On the basis of a retrospective analysis of the acquired data, a classification algorithm was developed, validated, and optimized. RESULTS: Intrinsic fluorescence, backscattered white light, and video imaging each contribute complementary information to diagnostic algorithms for high-grade cervical neoplasia. More than 10000 measurements that were made on colposcopically identified tissue from >500 subjects were the basis for algorithm training and testing. Algorithm performance demonstrated a sensitivity of approximately 90%. This performance was confirmed by various training methods. With the use of a multivariate classification algorithm, optical detection is predicted to detect 33% more high-grade cervical intraepithelial neoplasia (2/3+) than colposcopy alone. CONCLUSION: Full cervix optical interrogation for the detection of high-grade cervical intraepithelial neoplasia is feasible and appears capable of detecting more high-grade cervical intraepithelial neoplasia than colposcopy alone. With the use of this classification algorithm, a multisite, randomized controlled trial is underway that compares the combination of optical detection and colposcopy versus colposcopy alone.
Adolescent↗
Genetic algorithms for classification of olfactory stimulants.
We have developed and tested a genetic algorithm (GA) for pattern recognition, which identifies molecular descriptors that optimize the separation of the activity classes of olfactory stimulants in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by these descriptors is about differences between olfactory classes in the dataset. In addition, the GA focuses on those classes and or samples that are difficult to classify as it trains using a form of boosting to modify the fitness landscape. Boosting minimizes the problem of convergence to a local optimum, because the fitness function of the GA is changing as the population is evolving toward a solution. Over time, compounds that consistently classify correctly are not as heavily weighted in the analysis as compounds that are difficult to classify. The pattern recognition GA learns its optimal parameters in a manner similar to a neural network. The algorithm integrates aspects of both strong and weak learning to yield a "smart" one-pass procedure for feature selection and classification.
Algorithms↗
Gene selection and classification of microarray data using random forest.
BACKGROUND: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. RESULTS: We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. CONCLUSION: Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.
Algorithms↗
Fuzzy K-nearest neighbor classifiers for ventricular arrhythmia detection.
We report a study of the efficiency of 4 classifiers (the K-nearest-neighbor and single-nearest-prototype algorithms, each as parametrized by both Fuzzy C-Means and Fuzzy Covariance clustering) in the detection of ventricular arrhythmias in ECG traces characterized by 4 features derived from 7 spectral parameters. Principal components analysis was used in conjunction with a cardiologist's deterministic classification of 90 ECG traces to fix the number of trace classes to 5 (ventricular fibrillation/flutter, sinus rhythm, ventricular rhythms with aberrant complexes and 2 classes of artefact). Forty of the 90 traces were then defined as a test set; 5 different learning sets (numbering 25, 30, 35, 40 and 45 traces) were randomly selected from the remaining 50 traces; each learning set was used to parametrize both the classification algorithms using both fuzzy clustering algorithms and the parametrized classification algorithms were then applied to the test set. Optimal K for K-nearest-neighbor algorithms and optimal cluster volumes for Fuzzy Covariance algorithms were sought by trial and error to minimize classification differences with respect to the cardiologist's classification. Fuzzy Covariance clustering afforded significantly better perception of cluster structure than the Fuzzy C-Means algorithm, and the classifiers performed correspondingly with an overall empirical error ratio of just 0.10 for the K-nearest-neighbor algorithm parametrized by Fuzzy Covariance.
Algorithms↗
Microvascular reconstruction of the hypopharynx: defect classification, treatment algorithm, and functional outcome based on 165 consecutive cases.
The objectives of this study were three-fold: to develop a scheme for classification of hypopharyngeal defects, to establish a reconstructive algorithm based on this system, and to assess the functional outcome of such reconstruction. This study is a retrospective review of a 14-year experience with 165 consecutive microvascular reconstructions of the hypopharynx in 160 patients. The average patient age was 59 years (95 percent CI, 37 to 81). Thirty-four patients were operated on for recurrent disease; 71 had preoperative radiotherapy. Partial defects were reconstructed with radial forearm flaps (n = 52); circumferential defects were reconstructed with jejunum (n = 90); and extensive, noncircumferential longitudinal defects were reconstructed with rectus abdominis flaps (n = 23). The overall free flap success rate was 98 percent. Six flaps required reexploration, two of which were salvaged. The incidence of fistula was 7 percent and stricture, 4 percent. Preoperative radiotherapy was significantly associated with risk of recipient site complications (OR, 2.3; 95 percent CI, 1.0 to 5.0). Follow-up data were available on 95 percent of patients: 53 percent were able to tolerate an unrestricted diet, 23 percent a soft diet, 12 percent liquids only, and 12 percent were limited to tube feedings. The treatment algorithm for microvascular hypopharyngeal reconstruction is based on the type of defect with partial defects with radial forearm flaps, circumferential defects reconstructed with free jejunal flaps, and extensive, multilevel defects reconstructed with rectus abdominis myocutaneous flaps. Microvascular reconstruction of pharyngeal defects is highly successful with few postoperative complications. With appropriate flap selection, functional outcome can be optimized.
Adult↗