IJAIEM

V. Vasudha, P.V. Devika

Abstract

Common colloquialisms for kyphosis, which is defined as an inward arching of the upper back, include "roundback" and "hunchback" when the curvature is more pronounced. In most cases, compression or fractures in the spine cause this illness to manifest. Spinal anomalies or a gradual twisting of the spinal bones may cause additional types of kyphosis in children or teens. Kyphosis is more common in adolescence; however, it may appear at any age. It may be caused by a variety of things, including bad posture, developmental problems, and spinal anomalies. This study presents a machine learning strategy for kyphosis illness prediction, with the goals of bettering patient outcomes via earlier diagnosis. The goal of this study is to examine and compare various granularity levels of machine learning algorithms applied to biological data, including Decision Trees and Random Forests. The results highlight the importance of ML as a useful tool for dealing with biological issues in a wider