4 research outputs found

    The 2nd International Electronic Conference on Applied Sciences

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    This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network

    Artificial Intelligence Methods for Modelling Tremor Mechanisms

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    Tremors are one of the most common movement disorders primarily associated with various neurological diseases. Since there are more than 20 different types of tremors, differentiation between them is important from the treatment point of view. In the thesis, we focus on differentiation between three of the most common tremors: Parkinsonian, essential and mixed type of tremor. Our first goal was to build a diagnostic model for distinguishing between Parkinsonian, essential and mixed type of tremors, based on clinical examination data, family history and digital spirography. The process of building a model was carried out using argument-based machine learning which enabled us to build a decision model through the process of knowledge elicitation from the domain expert (in our case from a neurologist). The obtained model consists of thirteen rules that are medically sensible. The process of knowledge elicitation itself contributed to the higher classification accuracy of the final model in comparison with the initial one. In the final diagnostic model, attributes derived from the spirography were included in more than half of the rules. This motivated us to build a model based solely on the digital spirography data. For the needs of constructing an understandable model, we first built several attributes which represented domain medical knowledge. We have built more than 500 different attributes which were used in a logistic regression to construct the final diagnostic model. The model is able to distinguish subjects with tremors from those without tremors with 90% classification accuracy. During the process of attribute construction, we wanted to know what our attributes were detecting. Thus, we have developed a method for attribute visualisation on series. The method not only helped us with attribute construction, but it is also useful for visual interpretation of the diagnostic model's decisions. The visualisation method and consequently the decision model were evaluated with the help of three independent neurology experts. The results show that both the diagnostic model and the visualisation are meaningful and cover medical knowledge of the domain. The final diagnostic model is built into the freely available ParkinsonCheck mobile application

    Artificial Intelligence Methods for Modelling Tremor Mechanisms

    Get PDF
    Tremors are one of the most common movement disorders primarily associated with various neurological diseases. Since there are more than 20 different types of tremors, differentiation between them is important from the treatment point of view. In the thesis, we focus on differentiation between three of the most common tremors: Parkinsonian, essential and mixed type of tremor. Our first goal was to build a diagnostic model for distinguishing between Parkinsonian, essential and mixed type of tremors, based on clinical examination data, family history and digital spirography. The process of building a model was carried out using argument-based machine learning which enabled us to build a decision model through the process of knowledge elicitation from the domain expert (in our case from a neurologist). The obtained model consists of thirteen rules that are medically sensible. The process of knowledge elicitation itself contributed to the higher classification accuracy of the final model in comparison with the initial one. In the final diagnostic model, attributes derived from the spirography were included in more than half of the rules. This motivated us to build a model based solely on the digital spirography data. For the needs of constructing an understandable model, we first built several attributes which represented domain medical knowledge. We have built more than 500 different attributes which were used in a logistic regression to construct the final diagnostic model. The model is able to distinguish subjects with tremors from those without tremors with 90% classification accuracy. During the process of attribute construction, we wanted to know what our attributes were detecting. Thus, we have developed a method for attribute visualisation on series. The method not only helped us with attribute construction, but it is also useful for visual interpretation of the diagnostic model's decisions. The visualisation method and consequently the decision model were evaluated with the help of three independent neurology experts. The results show that both the diagnostic model and the visualisation are meaningful and cover medical knowledge of the domain. The final diagnostic model is built into the freely available ParkinsonCheck mobile application

    Predicting Parkinson's Disease with Voice Analysis on a Smartphone

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    Early diagnosis can have significant effect on disease progression, its treatment and patient's quality of life. However, some diseases are incurable, and doctors can only help relieve symptoms. One of such is Parkinson's disease, a neurodegenerative disease marked by tremor, slowness of movement, muscular rigidity and difficulty with speaking. The aim of this paper was to develop a system for early diagnosis of Parkinson's disease which could recognize signs of Parkinson's disease in a person's voice. For this purpose, a mobile application, an API interface and a classifier were developed. The API interface saves voice recordings made by the mobile application, then analyses and classifies them with the classifier. After the classification is done, the API interface sends the result back to the mobile application which informs its user about the outcome of their voice analysis. The application was developed for Android operating system. The API interface is based on the Flask library. Different classifiers using libraries Scikit-learn and Keras were developed. Then, the most appropriate classifier was chosen and implemented into the API interface. An example of how the application can be used is also described
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