Comparative Study and Detection of Spinal Deformities using Supervised Machine Learning Algorithms

Many issues with various parts of the body in the lumbar spine might cause lower back pain. Machine learning models used in the medical sector for illness diagnosis aid medical professionals in identifying diseases based on symptoms at a premature time. The goal of this is to have a look at and decide the maximum essential bodily traits that result in spinal deformities, in addition to also forecasting spinal malformation primarily based on the bodily facts accrued from the spine. Univariate function selection acts as a filterprimarily based on characteristic choice. For the identification of spinal abnormalities, a variety of machine learning methodologies such as Decision Tree Classifier, Support Vector Machine (SVM), and others are examined. The technique models are tested on a 310-sample dataset from the Kaggle repository. A number of parameters, including training and testing accuracy, recall, and f-1 score, are used to evaluate the performance of the classification of defective and healthy spinal patients.