Early Stage Detection of Scoliosis Using Machine Learning Algorithms

• Published in: 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS) Vol. 1; pp. 1 - 4
• Main Authors: Shrestha, Pratik, Singh, Aachal, Garg, Riya, Sarraf, Ishika, Mahesh, T R, Sindhu Madhuri, G
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21-12-2021
• Subjects: Classification algorithms; Costs; Linear regression; Linear Regression (LR); Machine Learning; Medical Images; Predictive analytics; Principle Component Analysis (PCA); Scoliosis; Security; Support vector machines; Task analysis
• DOI: 10.1109/FABS52071.2021.9702699

Scoliosis is the most common disease which is mainly identified from patient spine X-ray images. It is mainly diagnosed based on sideways curvature image modality. In scoliosis diagnosis detection, currently the treatment for scoliosis is based on spinal assessment manual study which has some limitations like it is very tedious, time-consuming and cost effective. Scoliosis diagnosis is a critical task in initial stages based on patient history records or on captured patient X-ray spine images. So our research work is carried out for detecting scoliosis in effective way by analyzing the quality of input X-ray images of the patient suffering from scoliosis. To overcome few limitations and for early stage predictive analysis detection of scoliosis, we develop a point-based automated method at different regions of spine which provides accurate results using various classification algorithms in this research paper. Predictive analysis is mainly analyzed using efficient and essential classification algorithms like Linear Regression (LR) and Support Vector Machine (SVM) and the performance metrics like Accuracy (A) and Elapsed Time (ET) are compared to benefit the patients suffering from scoliosis with less time, low cost and by improving the quality of input images.