Effective Kyphosis Disease Prediction Using Machine Learning Algorithms
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 9
Abstract
Kyphosis is the term used to describe the inward arching of the upper back. This specific ailment is sometimes referred to as"round back" or “hunchback” if there is a noticeable curvature. Kyphosis often occurs due to weakened spinal bones, leading to compression or fractures. Other forms of kyphosis in children or adolescents may result from spinal abnormalities or a progressive twisting of the spinal bones. While it can occur at any age, kyphosis is most common in teenagers. Many factors, from poor posture and developmental problems to structural abnormalities in the spine, can lead to kyphosis. This research introduces a machine learning approach to predict kyphosis disease, aiming to enhance early detection and improve patient outcomes. The main goal of this work is to apply various machine learning techniques to biological data, including Random Forest and Decision Tree, and to evaluate the accuracy of the algorithms. This shows that machine learning (ML) is a valuable tool that should be used to solve biological problems in general
Authors and Affiliations
R Siva, V Hemambica, B Avanthika, S Tharun Sai
Keywords
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- EP ID EP747887
- DOI https://doi.org/10.46501/IJMTST1009008
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