• Resham N. Waykole
• Anuradha D. Thakare

Lower back pain (LBP) is a common medical problem which is suffered by many individuals during their normal lifestyles and keeps them from routine activities. Diagnosing LBP is challenging since it requires highly specialized knowledge involving a complex anatomical and physiological structure as well as diverse clinical considerations. LBP is often accompanied by hyperactivity of superficial paraspinalmusclesandithasbeensuggested that psychological factors may affect the condition via increased spinal loading resulting from altered paraspinal muscle activity. Several measurements are taken into consideration which includes physical factors such as muscle activity, pain intensity, disability and psychosocial factors such as anxiety, depression, fear of movement etc using several numerical scales and questionnaires. Applying machine learning techniques on such data can obtain relationships between these measurements which can help in diagnosis and classification of LBP. This work aims at studying and analyzing the machine learning techniques for classification of LBP into major categories like normal and abnormal spine conditions. Mainly machine learning techniques such as K-Nearest Neighbors, Decision Tree, Artificial Neural Network, Support Vector Machine, Naive Bayes, Deep Neural Network were used. The results indicate the deep neural network performs better than other techniques.

K NN,SVM,LBP,SVD,Deep Learning, DNN.

"CLASSIFICATION OF LOW BACK PAIN USING DEEP LEARNING NEURAL NETWORK MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.783-786, September-2018, Available :http://www.jetir.org/papers/JETIR1809762.pdf

"CLASSIFICATION OF LOW BACK PAIN USING DEEP LEARNING NEURAL NETWORK MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp783-786, September-2018, Available at : http://www.jetir.org/papers/JETIR1809762.pdf