Abstract
Heart disease is harder to diagnose as the symptoms build up before being treated immediately. Fortunately, with the advancement in technology, it has become easier to predict by analyzing past data. This paper presents Machine Learning algorithms like logistic regression, Naïve Byes, Random forest classifier, Extreme Gradient Booster, K-Nearest Neighbor, Decision Tree Classifier, Support Vector Machine, along with neural network techniques like Dense Neural Networks and Convolutional Neural Networks. Different visualization tools analyze the data, and feature selection methods like Information Gain, Fisher Score, and correlation coefficient select the best features. The accuracies and results are compared accordingly.
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Mane, V., Tobre, Y., Bonde, S., Patil, A., Sakhare, P. (2023). Heart Disease Prediction Using Machine Learning and Neural Networks. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_17
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DOI: https://doi.org/10.1007/978-981-19-7892-0_17
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