An Approach for Predicting Disease in the Heart Using an Improved Deep Learning Algorithm
Keywords:
heart disease, Convolutional Neural Network (CNN), accuracy, deep learning, PythonAbstract
The heart is a vital organ in humans. Deaths from heart disease are common, regularly recorded, and rapidly rising. There is no method to foresee illness or remedy the issue. The research aim is to create a deep learning-based artificial intelligence system for heart disease identification. The deep learning method is a great resource for illness prediction of all types. Better activation functions are used in the convolutional layers of the improved convolutional neural network (ICNN) that is proposed in this paper. A dense or fully connected layer is used to combine the key features. By contrasting the proposed ICNN algorithm with other deep algorithms already in use, its efficiency is verified. This article tests the deep learning algorithm's accuracy in predicting heart disease using data from the UCI repository. The Python Jupyter environment is used for the implementation. The effectiveness of the suggested ICNN model is determined using performance indicators like precision, accuracy, F1 score, along with recall.
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