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

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

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)