Diagnosis of Brain Hemorrhage Using KNN based Radial Basis Classifier

Abstract

There occurs a peak interest in medical diagnosis with the aid of machine learning. One of the common old age disease is the Alzheimer disease and it is significant for accurate diagnosis of Brain hemorrhage in AD patients on the basis of image processing and machine learning approaches. Brain hemorrhage is the leading death cause between the age group of fifteen and twenty four. Lifesaving of such kind of patients is crucial and totally depends on the identification of the exact location and hemorrhage type in very early stage. The proposed method designed a fully automated machine learning approach for the classification by the novel KNNRBF method of disease with respect to its severity levels (Brain hemorrhage, PICS and Alzheimer disease) even in the absence of an expert. The performance analysis of the proposed system with respect to the performance measures like accuracy, specificity, sensitivity, Jaccard coefficient, kappa and dice coefficient respectively. The future work deals with the implementation of the model in deep learning and in realtime as a radiology optimization tool.

Prognosis of Chronic Diseases Using Different Machine Learning Models

“…Efficient Pre-processing is the key process to get high accuracy. Jathin Desan [19], The segmentation by k-means clustering, Gaussian Filtering, extracting features by DWT and GLCM followed by KNNRBF classification. the extracted features with KNNRBF is most optimal for patient data processing In this paper, Models Learning for identifying contributing factors from Patient's Historical Data.…”