Abstract—machine Learning Techniques Are Widely Used Now for Neuro-imaging Based Diagnosis. These Methods Yield Fully Automated Clinical Decisions, Unbiased by Variable Radiological Expertise. This Research Paper Compares and Evaluates the Performance and Reliability of Conventional Least Square Sup

Machine learning techniques are widely used now for neuro-imaging based diagnosis. These methods yield fully automated clinical decisions, unbiased by variable radiological expertise. This research paper compares and evaluates the performance and reliability of conventional Least Square Support Vector Machine (LSSVM) with that of Particle Swarm Optimization (PSO) based LSSVM in the diagnosis of dementia. The manual interpretation of large volume of brain MRI and cognitive measures may lead to incomplete diagnosis. The PSO-LSSVM approach is trained with multiple biomarkers to facilitate effective, accurate classification which is a requirement of the hour. Wavelet based texture features and multiple biomarkers are fed as input to the classifier. PSO-LSSVM yields 98% accurate results and outperforms LSSVM classifier in terms of sensitivity, specificity and accuracy in this analysis

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