No abstract

Recently huge amount of data is available in the field of medicine that helps the doctors in diagnosing diseases when analysed. Data mining techniques can be applied to these medical data to extract knowledge so that disease prediction becomes accurate and easier. In this work, cardiotocogram (CTG) data is analysed using Support Vector Machine (SVM) for predicting fetal risk. Opposition based firefly algorithm (OBFA) is proposed to extract the relevant features that maximise the classification performance of SVM.The obtained results show that opposition based firefly algorithm outperforms the standard firefly algorithm (FA).

In this modern era, various cardiac diseases are very crucial as they cause a high mortality rate. Early detection of Cardio Vascular Disease (CVD) is essential to prevent and control it. Diagnosis of cardiac disease is the process of analyzing the Left and Right ventricle cavities (LV, RV) and Myocardium (MYO) from Cardiac Magnetic Resonance (CMR) images. As the deep learning architectures are becoming more mature, segmenting and classifying cardiac MRI images using deep learning is gaining more attention. This work is aimed to identify five different cardiac disease subgroups namely NOR, MINF, DCM, HCM and ARC by employing a new Deep Join Attention Model (DJAM) technique for segmenting LV, MYO, and RV regions separately.This method provides advancement as the joined attention model was combined with the pooling layers and the resultant is added to the convolution layers. The proposed Region Integrated Deep Residual Network (RIDRN) is used to extract the features from the segmented images for classification. In this process, the features of LV, RV and MYO are combined with a different combination. The advantage of doing this process is to get the overall features without leaving any single strip of features from the three regions. Hence it shows a rise in the performance accuracy. The Random Forest classification method is used to classify the underlying features for cardiac disease diagnosis.This proposed work is tested in the Automated Cardiac Diagnosis Challenge (ACDC) dataset and it perfects the state-of-art techniques.