No abstract

The Phenomenon of heart disease prediction has been well studied. There exist numerous techniques exist in literature which uses different features and methods. However, the accuracy of predicting heart disease is still a questioning factor. Towards improving the performance of heart disease prediction an efficient Integrated Deep Learning Model with Convolution Neural Network (IDLM_CNN) is presented in this article. The model considers various features from different data sets of lungs, diabetic and clinical features. The integrated model extracts texture features from lung images in form of mass values. Similarly, the blood glucose, BMI and other diabetic features are extracted from diabetic data set. Also, lifestyle features like physical habits, food habits and smoking habits are extracted from clinical data sets. Such features extracted from various data sets are combined and trained with Convolution neural network to support the disease prediction. The method convolves the features of lungs and combines with other features to compute Disease Prone Weight (DPW) towards cardiac disease. Based on the value of DPW, the method predicts the possibility of heart disease.  The proposed method increases the performance of disease prediction and reduces the false ratio.

In present scenario, 72% of all fatalities globally are caused by lung cancer, which is the cellular fission of aberrant cells within the lungs. With a 19% likelihood of survival, lung cancer is also known as one of the major causes of death. A number of techniques, including X-rays, CT scans, biopsies, and others, can be used to diagnose tumors. The Computer Tomography (CT) scan technique is one of the most effective methods for detecting lung cancer early among the methods mentioned above. An early and precise diagnosis is crucial for the disease’s treatment. The creation of multiple machine learning algorithms can effectively forecast outcomes. The accuracy of the model in classifying diseases, however, is significantly influenced by the model’s complexity and the decision of the appropriate machine learning approach for the given dataset. To address this issue, this work develops a Fuzzy-based Intelligent Model for Lung Cancer Classification (FID-LCC). The wavelet cleft fuzzy algorithm can be used in the model to aggregate the features from the obtained images. Then, using Improved Binary Particle Swarm Optimization (IBPSO), the anomalous features can be selected. Following that, classification is accomplished using convolutional neural networks (CNN). The simulation findings demonstrate that the proposed strategy’s accuracy in determining the Lung Cancer is greater to other traditional methods when the classifier’s performances are compared.