The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
The spectral analysis and spatial analysis of high dimensional images are very important and in this paper we tried to cover some aspects that how this problem can be handled and proposed a way through which we can overcome the problem of the time constraint and using some deep learning novel approach like transfer learning for getting the best results while performing the actual computations and the results which we obtained. The dataset used is EuroSAT in which by using a VGG network, the accuracy is achieved 95 per cent and the validation accuracy achieved is 92 per cent. Also, the Kappa score which we got for this observation is 0.95. The tool used for the implementation purpose is TensorFlow with GPU which is also discussed in the paper.
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