Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.

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Since the last decade, the Electrocardiogram (ECG) tool has received medical experts and researchers' attention for accurate and fast diagnosis of cardiovascular diseases (CVD). The automatic detection and classification of 1D ECG-based heart disease have become more realistic and efficient solutions using the deep learning technique such as Convolutional Neural Network (CNN). As CNN is designed mainly for 2D or 3D applications therefore, designing CNN to process 1D ECG becomes challenging. We proposed a novel framework for automatic CVD detection and classification from the raw ECG signals using 1D-CNN deep learning technique. The framework consists of pre-processing, automatic feature extraction, feature optimization, and classification. In pre-processing, raw ECG signals are filtered to remove the baseline drift and power line interference using median and notch filters respectively. The pre-processed ECG signals are then used to extract the QRS and ST waves using the dynamic thresholding in the wavelet transfer domain. The fusion of QRS and ST waves have fed to automatic 1D-CNN that consists of layers i.e,1D convolutional layer, ReLU layer, and max-pooling layers. The 1D-CNN is proposed in this paper to extract features automatically with little computing complexity. The high-dimensional raw CNN features are optimized by applying a feature selection and scaling approach. For classification, different soft computing techniques such as Long- Short Term Memory (LSTM), Support Vector Machine (SVM), Naïve Bays (NB), Artificial Neural Network (ANN), and k-nearest neighbor (KNN) are applied. The experimental performances of the proposed model have been investigated on a publicly available research dataset and outperformed recent CNN-based techniques.

Disease prediction is one of the most important issues that we are facing today. A large number of patients struggling for their check up even for predictive disease like heart attack possibilities, kidney damage change and possibilities of lung problem. All these lies in predictive disease categories. They need not require very vast analysis if we can predict. This Research motivate to develop a console(GUI) on the basis of data mining which is used to analyze large volumes of data and extracts information that can be converted to useful knowledge. And overall predict a patient for their chances of disease. These techniques can be applied on predictive medical disease. This research papers which mainly concentrated on predicting kidney failure, heart disease. Experimental results will show that many of the rules help in the best prediction of heart disease and kidney failure which even helps doctors in their diagnosis decisions by using Aprior and k-mean algorithm. By the help of this algorithm it provide easy and efficient way in which we can find the stage of the kidney failure and heart disease. To swamp this problem the healthcare industry gathers enormous amounts of heart disease data which, grievously, are not "mined" to discover hidden information for effective decision making. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. So due to these condition even doctors not able to predict disease accurately. So there is need to develop a efficient decision making system which can predict the correct diseases with available data. So in this paper we are introducing the automated console to predict the diseases by mean of clustering & a-priori algorithm. This is web based convenient tool it can be used even in absence to doctors to predict diseases. Here, we consider almost 200 persons data to develop this automated console. Preliminary conclusions shows that it very effective tool to predict diseases.