66 research outputs found
Direct-to-Patient Survey for Diagnosis of Benign Paroxysmal Positional Vertigo
Given the high incidence of dizziness and its frequent misdiagnosis, we aim to create a clinical support system to classify the presence or absence of benign paroxysmal positional vertigo with high accuracy and specificity. This paper describes a three-phase study currently underway for classification of benign paroxysmal positional vertigo, which includes diagnosis by a specialist in a clinical setting. Patient background information is collected by a survey on an Android tablet and machine learning techniques are applied for classification. Decision trees and wrappers are employed for their ability to provide information about the question set. One goal of the study is to attain an optimal question set. Each phase of the study presents a unique set and style of questions. Results achieved in the first two phases of the survey indicate that our approach using decision trees with filters or wrappers does a good job of identifying benign paroxysmal positional vertigo
Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.
Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis
On Knowledge Discovery Experimented with Otoneurological Data
Diagnosis of otoneurological diseases can be challenging due to similar kind of and
overlapping symptoms that can also vary over time. Thus, systems to support and
aid diagnosis of vertiginous patients are considered beneficial. This study continues
refinement of an otoneurological decision support system ONE and its knowledge
base. The aim of the study is to improve the classification accuracy of nine
otoneurological diseases in real world situations by applying machine learning
methods to knowledge discovery in the otoneurological domain.
The phases of the dissertation is divided into three parts: fitness value formation
for attribute values, attribute weighting and classification task redefinition. The first
phase concentrates on the knowledge update of the ONE with the domain experts
and on the knowledge discovery method that forms the fitness values for the values
of the attributes. The knowledge base of the ONE needed update due to changes
made to data collection questionnaire. The effect of machine learnt fitness values on
classification are examined and classification results are compared to the knowledge
set by the experts and their combinations. Classification performance of nearest
pattern method of the ONE is compared to k-nearest neighbour method (k-NN)
and NaĂŻve Bayes (NB). The second phase concentrates on the attribute weighting.
Scatter method and instance-based learning algorithms IB4 and IB1w are applied in
the attribute weighting. These machine learnt attribute weights in addition to the
weights defined by the domain experts and equal weighting are tested with the
classification method of the ONE and attribute weighted k-NN with One-vs-All
classifiers (wk-NN OVA). Genetic algorithm (GA) approach is examined in the
attribute weighting. The machine learnt weight sets are utilized as a starting point
with the GA. Populations (the weight sets) are evaluated with the classification
method of the ONE, the wk-NN OVA and attribute weighted k-NN using
neighbour’s class-based attribute weighting (cwk-NN). In the third phase, the effect
of the classification task redefinition is examined. The multi-class classification task
is separated into several binary classification tasks. The binary classification is studied
without attribute weighting with the k-NN and support vector machines (SVM)
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Mining balance disorders' data for the development of diagnostic decision support systems
In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts
Improving Benign Paroxysmal Positional Vertigo Diagnosis
Benign Paroxysmal Positional Vertigo (BPPV) is one of the most common causes of dizziness. Especially for people over 45, the risk of BPPV is substantial. On the other hand, BPPV is often misdiagnosed and may require expensive examinations. This thesis introduces a prediction model based on machine learning to quickly, inexpensively, and accurately diagnose BPPV. The thesis starts by introducing BPPV and the statistics of BPPV misdiagnosis. Then, a patient survey is introduced. The patient survey includes 50 BPPV-related questions, which are used as training data for the machine learning model. Logistic Regression, Decision Tree, and NaĂŻve Bayes were compared for machine learning models and their results were discussed. Three machine learning approaches are explored, logistic regression, decision tree, and naĂŻve Bayes with cross validation accuracies of 89.8%, 81.9%, and 75.1%, respectively
Predictive Capability of an iPad-Based Medical Device (medx) for the Diagnosis of Vertigo and Dizziness
Background:Making the correct diagnosis of patients presenting with vertigo and dizziness in clinical practice is often challenging. Objective:In this study we analyzed the usage of the iPad based program medx in the prediction of different clinical vertigo and dizziness diagnoses . We examined the power of medx to distinguish between different vertigo diagnoses. Patients and methods:The data collection was done in the outpatient clinic of the German Center of Vertigo and Balance Disorders. The “gold standard diagnosis” was defined as the clinical diagnosis of the specialist during the visit of the patient standardized history and clinical examination. Another independent and blinded physician finalized each patient’s case in constellatory diagnostic of medx by entering all available clinical information in the system. The accuracy, sensitivity, specificity as well as positive and negative predictive values for the most common diagnoses were determined. Sixteen possible different vertigo and dizziness diagnoses could be provided by medx constellatory diagnostic system. These diagnoses were compared to the “gold standard” by retrospective review of the charts of the patients over the study period. Results:610 patients (mean age58.1±16.3 years, 51.2 female) were included. The accuracy for the most common diagnoses was between 82.1- 96.6 with a sensitivity from 40- 80.5 and a specificity of more than 80. When analyzing the quality of medx in a multiclass-problem for the six most common clinical diagnoses the sensitivity, specificity, positive and negative predictive value were as follows: Bilateral vestibulopathy (81.6, 97.1, 71.1, 97.5), Menière's disease (77.8, 97.6, 87., 95.3), benign paroxysmal positional vertigo (61.7, 98.3, 86.6, 93.4), downbeat nystagmus syndrome (69.6, 97.7, 71.1, 97.5), vestibular migraine (34.7, 97.8, 76.1, 88.3) and phobic postural vertigo (80.5, 82,5, 52.5, 94.6), Conclusions:This study demonstrates that medx is a new and easy approach to screen for different diagnoses. With the high specificity and high negative predictive value the system helps to rule out differential diagnoses and can therefore also lead to a cost reduction in health care system. However, the sensitivity was unexpectedly low, especially for vestibular migraine. All in all, this device can only be a complementary tool, in particular for non-experts in the field
On classification in the case of a medical data set with a complicated distribution
Abstract In one of our earlier studies we noticed how straightforward cleaning of our medical data set impaired its classification results considerably with some machine learning methods, but not all of them, unexpectedly and against intuition compared to the original situation without any data cleaning. After a more precise exploration of the data, we found that the reason was the complicated variable distribution of the data although there were only two classes in it. In addition to a straightforward data cleaning method, we used an efficient way called neighbourhood cleaning that solved the problem and improved our classification accuracies 5–10%, at their best, up to 95% of all test cases. This shows how important it is first very carefully to study distributions of data sets to be classified and use different cleaning techniques in order to obtain best classification results.Peer reviewe
Parameterizing neural networks for disease classification
Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency
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