A hybrid classification and prediction methodology for the diagonosis of osteoporosis

Abstract

With the advent of technology medical science is growing very fast. Disease diagonosis using machine learning technique is quite cumbersome. But efforts are made by the innovative minds to develope an optimal and efficient prediction model for the prediction of the disease viz, bone disease. Bone disease prediction is also a broad area of research where machine learning techniques can be used. Better prediction and analysis can be helpful to cure such disease. Osteoporosis is an osteo-metabolic disease characterized by low bone mineral density (BMD) and deterioration of the micro-architecture of the bone tissues, causing an increase in bone fragility and consequently leading to an increased risk of fractures.. Machine learning algorithms play important role for predicting and analyzing such disease using available algorithms and by modifying them. There are many algorithms such as SVM (Support vector machine), Genetic Algorithm, Naive bayes classifier and other tree based classifiers which are proposed in traditional scenario for prediction of expected data from an image. The goal of this paper is to discuss about a proposed hybrid analysis and prediction approach for diagonosing osteoporosis. This paper also discuss about various prediction models viz, svm prediction model as the efficient one while processing the textual data for symptoms analysis. Here symptoms are called predictors. A comparison using the Accuracy and training time is performed. The approach shows the efficiency of proposed model over textual data as well as graphical image data analysis.