Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier

Abstract:This paper presents the results of applying machine learning algorithms to predict hearing loss symptoms given air and bone conduction audiometry thresholds. FP-Growth (frequent pattern growth) algorithm was employed as a feature extraction technique. The effect of extracting naïve Bayes classifier's vocabulary from patterns generated by FP-Growth algorithm was explored. Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. The results were validated with repeated random sub-sampling validation performed using 5 partitions with 10, 20, 30, 40 and 50 training examples respectively averaged over 10 iterations. The multivariate Bernoulli model with feature extraction is found to be more accurate in predicting hearing loss symptoms with average error rate of only 0, 0.5, 1, 1.75 and 5.4% for the partitions with 10, 20, 30, 40 and 50 training examples respectively compared to multinomial model with feature extraction. However, the two models with feature extraction produce better results than same models without feature extraction

Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm

There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. So often, clinicians rely in their skills and experience and that of other medical experts as their source of information. The healthcare sector is now capturing more data in the form of digital and non digital format that may potentially be mined to generate valuable insights. In this paper we propose a five step knowledge discovery model to discover patterns in medical audiology records. We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. Our aim is to discover interesting itemsets that shows connection between hearing thresholds in pure-tone audiometric data and symptoms from diagnosis and other attributes in the medical records. The experimental results are summaries of frequent structures in the data that contains symptoms of tinnitus, vertigo and giddiness with threshold values and other information like gender

Measuring the impact of the digital economy in developing countries: A systematic review and meta- analysis

The digital economy, driven by Information and Communication Technology (ICT), has emerged as a significant contributor to economies worldwide. However, accurately defining and measuring its impact on national economies remains a complex endeavor. This paper explores the definition, measurement, role, and impacts of the digital economy across various economies. It also examines the involvement of governments and telecommunication regulators in assessing the digital economy and identifies future directions for developing countries. A systematic literature review utilizing the PRISMA Model is employed to investigate the factors and indices used to measure the digital economy. The findings highlight ongoing efforts to harmonize the definition and metrics; nonetheless, challenges persist due to the scarcity of appropriate datasets and variations in country-specific definitions. Additionally, the effectiveness of existing digital economy indices and toolkits in assessing the level of digitalization in developing countries is evaluated. The paper concludes that despite ongoing efforts to bridge the gaps, the concept of the digital economy remains defined and measured differently, necessitating a new definition that accounts for various contextual peculiarities. Furthermore, a roadmap is proposed to develop a toolkit that ensures comprehensive measurement, thus preventing an underestimation of the digital economy's contribution to the Gross Domestic Product (GDP) in developing countries. The paper underscores the need for international and multi-stakeholder dialogue to establish a common understanding of the digital economy's definition and measurement. Developing countries, such as Nigeria, are urged to develop or adopt new metrics tailored to their unique circumstances, facilitating an accurate and efficient quantification of the digital economy's impact on crucial indicators like GDP. Improved statistical data collection and recording methodologies are recommended for both governments and the private sector. Moreover, the paper advocates for the establishment of a Digital Economy Advisory Board (DEAB) in developing countries to maximize the benefits of the ongoing global transition to the digital economy

Identifying Relationship between Hearing loss Symptoms and Pure-tone Audiometry Thresholds with FP-Growth Algorithm

Considerable numbers of studies have related audiometry hearing threshold values with various diseases and conditions that cause hearing loss. The purpose of this study was to find the relationship that exists between pure-tone audiometry threshold values and hearing loss symptoms in a medical datasets of 339 hearing loss patients using association rule mining algorithm. FP-Growth (Frequent Pattern) algorithm is employed for this purpose to generate itemsets given 0.2 (20%) as the support threshold value and 0.7 (70%) as the confidence value for association rule generation. Interesting relationships were discovered and the results were compared to earlier findings using the same method on a sample datasets of 50 hearing loss patients with 0.1 as the minimum support and 0.7 confidence thresholdsfor the association rule mining. There is similarity in the correlation that exists between symptoms and the pure-tone hearing thresholds from the initial study results and the correlation in the current study results. The experimental result with 339 patients medical datasets extends previously published findings on 50 patients’ medical datasets and the sets of symptoms that appear together is consistent with current knowledge of those symptoms occurring together as evidenced clinically

Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier

This paper presents the results of applying machine learning algorithms to predict hearing loss symptoms given air and bone conduction audiometry thresholds. FP-Growth (frequent pattern growth) algorithm was employed as a feature extraction technique. The effect of extracting naïve Bayes classifier’s vocabulary from patterns generated by FP-Growth algorithm was explored. Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. The results were validated with repeated random sub-sampling validation performed using 5 partitions with 10, 20, 30, 40 and 50 training examples respectively averaged over 10 iterations. The multivariate Bernoulli model with feature extraction is found to be more accurate in predicting hearing loss symptoms with average error rate of only 0, 0.5, 1, 1.75 and 5.4% for the partitions with 10, 20, 30, 40 and 50 training examples respectively compared to multinomial model with feature extraction. However, the two models with feature extraction produce better results than same models without feature extraction

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively