Conference Papers

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    Hearing Loss Prediction using Machine Learning Approaches: Contributions, Limitations and Issues
    (Institute of Electrical and Electronics Engineers Inc., 2022) Pai, P.K.; Santhi Thilagam, P.S.
    Hearing, one of the five basic human senses is the ability to perceive sounds and give meaning to them. Hearing loss is a significant health problem affecting children and adults and is growing exponentially. There is a lack of knowledge regarding hearing loss despite enough awareness, resulting in detection and treatment delays. The need for detection at an early stage is significant so that people can take necessary precautions given the limited options for treatment. This paper aims to survey machine learning-based hearing loss prediction. We investigate datasets, machine learning methods, and their outcomes. We also discuss the constraints, difficulties, and intended future works. Based on the results of this survey, we have a greater understanding of the problem's complexity, the obstacles to developing a better system, and the scope of the research, which has led us to concentrate our efforts in the future on analysing data from newborns, infants, and young children. © 2022 IEEE.
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    Hearing Loss Prediction in Newborns, Infants and Toddlers using Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Pai, P.K.; Santhi Thilagam, P.S.
    Hearing is one of the five senses critical to a person's day-to-day functioning. Despite enough awareness, society still has a stigma around hearing loss. It is one of the significant problems in the world today and is increasing exponentially. Early detection and intervention is the way to prevent and treat this problem. This paper focuses on predicting hearing loss in newborns, infants, and toddlers. First, the data is generated for the focused population in cooperation with an audiologist. Then, classification algorithms are applied to the data generated to build predictive models to determine hearing loss. Naïve Bayes, Support Vector Machines, XGBoost and Random Forest are the algorithms used for classification. Two datasets are generated, one with all classes having an equal number of records (balanced) and the other considering the prevalence of loss in population and noise (imbalanced). Maximum accuracy of 100% is obtained for the balanced dataset and 94.10% for the imbalanced dataset from Support Vector Machines. © 2022 IEEE.
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    Utilizing Machine Learning for Lung Disease Diagnosis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Markose, G.C.; Sitaraman, S.R.; Kumar, S.V.; Patel, V.; Mohammed, R.J.; Vaghela, C.
    For lung issues to be really treated and made due, early location and analysis are fundamental. In healthcare, machine learning (ML) strategies have arisen as an expected innovation with quick development, particularly in the field of clinical diagnostics. To analyze lung diseases, this research investigates the utilization of machine learning calculations. It centers around picture examination, patient information understanding, and the reconciliation of numerous information hotspots for an intensive investigation. This research's principal objective is to explore the chance of utilizing machine learning calculations to foresee and analyze a scope of lung conditions, including lung malignant growth, bronchitis, asthma, sensitivities, and persistent obstructive pneumonic disease (COPD). Proactive mediation depends on expecting the probability of lung issues before they manifest. Utilizing an assortment of machine learning techniques for classification and expectation, the examination assembled a heterogeneous dataset fully intent on laying the preparation for protection healthcare measures. © 2024 IEEE.