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

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

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 Naïve 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)

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 Naïve Bayes were compared for machine learning models and their results were discussed. Three machine learning approaches are explored, logistic regression, decision tree, and naïve 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

Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry

BACKGROUND Spontaneous episodic vertigo syndromes, namely vestibular migraine (VM) and Menière's disease (MD), are difficult to differentiate, even for an experienced clinician. In the presence of complex diagnostic information, automated systems can support human decision making. Recent developments in machine learning might facilitate bedside diagnosis of VM and MD. METHODS Data of this study originate from the prospective patient registry of the German Centre for Vertigo and Balance Disorders, a specialized tertiary treatment center at the University Hospital Munich. The classification task was to differentiate cases of VM, MD from other vestibular disease entities. Deep Neural Networks (DNN) and Boosted Decision Trees (BDT) were used for classification. RESULTS A total of 1357 patients were included (mean age 52.9, SD 15.9, 54.7% female), 9.9% with MD and 15.6% with VM. DNN models yielded an accuracy of 98.4 ± 0.5%, a precision of 96.3 ± 3.9%, and a sensitivity of 85.4 ± 3.9% for VM, and an accuracy of 98.0 ± 1.0%, a precision of 90.4 ± 6.2% and a sensitivity of 89.9 ± 4.6% for MD. BDT yielded an accuracy of 84.5 ± 0.5%, precision of 51.8 ± 6.1%, sensitivity of 16.9 ± 1.7% for VM, and an accuracy of 93.3 ± 0.7%, precision 76.0 ± 6.7%, sensitivity 41.7 ± 2.9% for MD. CONCLUSION The correct diagnosis of spontaneous episodic vestibular syndromes is challenging in clinical practice. Modern machine learning methods might be the basis for developing systems that assist practitioners and clinicians in their daily treatment decisions

Benign Paroxysmal Positional Vertigo Predictive Diagnosis from Patient-facing Survey

Benign Paroxysmal Positional Vertigo (BPPV) is a leading cause of dizziness and imbalance that is responsible for one-third of fall incidents. Diagnosis, however, is ridden with uncertainties and errors. This thesis explores various techniques for BPPV predictive diagnosis from a survey study and proposes measures for predictive performance improvement. Patient-facing surveys are established ways of acquiring medical history in clinical settings and, as this thesis demonstrates, are capable of conveying patterns distinguishable for accurate diagnosis. This work begins by discussing BPPV and vestibular disorders in general, and the risks associated with misdiagnosis or elusive diagnosis. Innovative efforts by medical professionals in vestibular therapy for handling the intricacies of diagnosis and clinical protocols are also explained. To predict BPPV successfully, there are distinguishing marks present in a patient’s dizziness episodic history including the frequency and duration of episodes, the specific nature of the dizziness, and the positional trigger. Given these indicators for predicting BPPV, we develop a number of statistical models on a dataset of survey responses acquired from a clinical cohort study. Next, the thesis establishes a connection between the performance limits of the machine learning methods, and the existence of incorrect answers to the survey prompts. By demonstrating that question misinterpretation and ambiguities exist in the cohort study, we show that certain data quality improvement measures have significant influence on classification performance