International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Deep Learning-Based Approach for Thyroid Dysfunction Prediction Tushar Bhatia Student, Department of Computer Science and Engineering, HMR Institute of Technology and Management, Delhi, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – Globally, thyroid dysfunction is a major health
disorders. It can affect bodily functions like energy production, weight management, and mood regulation.
concern caused due to irregular hormone production by the thyroid gland. Millions of populations are getting affected by this disease regularly. Accurate diagnosis of thyroid dysfunction is crucial for effective treatment and management of the disease, but this is challenging given the condition’s complex and varied symptoms. In this paper, a deep learningbased neural network algorithm for generating predictions is constructed based on a dataset of approximately 3772 patient records with 28 features. The Artificial Neural Network (ANN) model was trained and evaluated using standard machine learning techniques and achieved high-level accuracy (98.8%) in identifying instances of thyroid dysfunction. The findings demonstrate that the proposed ANN model can be a reliable and effective tool for early diagnosis of thyroid dysfunction. The suggested model has several advantages, including its ability to handle a large number of input parameters and its ability to learn intricate relationships between input and output variables. However, further research is required to assess if the suggested approach can apply to more extensive and diverse patient populations. Overall, the results of this study lay out the potential of machine learning and ANN models in the diagnosis of thyroid dysfunction and may aid in creating more precise and effective diagnostic equipment for this prevalent endocrine illness.
Symptoms of thyroid dysfunction can vary widely and include fatigue, weight gain, depression, and anxiety. Early detection and treatment of thyroid disorders are essential for managing the condition and avoiding severe complications. Diagnosing thyroid dysfunction requires a combination of clinical evaluation, biochemical tests, and imaging techniques. However, traditional diagnostic methods are time-consuming, expensive, and require specialized tools and expertise. Therefore, there is a need for a methodical and accurate approach to the identification of thyroid disorder. Deep Learning-based model architecture has emerged as a convincing technique for improving the efficiency of thyroid dysfunction prediction. This paper presents a Deep Learning Artificial Neural Network (ANN) model for making a prediction using clinical and biochemical parameters.
Key Words: Thyroid Dysfunction, Deep Learning, Neural Network, Artificial Neural Network, Machine Learning, accuracy, endocrine illness.
1.INTRODUCTION The thyroid gland is a tiny, butterfly-shaped organ situated in the front of the neck, surrounding the windpipe. Our body contains glands, which produce and release compounds that help the body to perform a specific function. The thyroid gland produces hormones, namely levothyroxine(T4) and triiodothyronine(T3), which assist in regulating metabolism, heart rate, body temperature, and other essential processes. When the thyroid gland is overactive or inactive, it can lead to various health problems.
Fig -1: Thyroid gland
1.1 Deep Learning Deep Learning lies within the strata of machine learning (ML) and artificial intelligence (AI). Its methodology is influenced by the human brain's structure and function. It involves training artificial neural networks, which are complex mathematical models that can learn to recognize patterns in data.
Thyroid dysfunction is a widespread endocrine disorder affecting millions worldwide, irrespective of age, gender, and ethnicity. It occurs when the thyroid gland either produces excess or insufficient hormones, which can result in several health issues. Hypothyroidism, characterized by low thyroid hormone levels, and hyperthyroidism, characterized by high thyroid hormone levels, are the most common thyroid
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Deep Learning has risen in prominence in recent years, owing to the abundance of extensive amounts of data and powerful computing resources. It has enabled significant advances in several fields like natural language processing, computer vison, speech recognition, and medical science.
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