Abstract
We know that the main advantage of building a prediction model based on Machine Learning is to predict future outcomes depending on the historic data. In recent times, Deep learning gained popularity due to its applications in image processing in Neural networks (NN). However, the disadvantage is due to its black box approach. Deep learning models perform efficiently on large data sets. It may not perform well in all the cases when the data is small in size. In this paper, a comparative study is being done on Deep Learning and Traditional Machine Learning algorithms like SVM, Random Forest, KNN, Gradient Boosting, AdaBoost, Naive Bayes, Neural Networks, and Decision Tree. In this paper, Diabetes, Blood Cancer, Heart disease datasets are used to study and compare the performance accuracy.
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Padmaja, D.L., Surya Deepak, G., Sriharsha, G.K., Ramana Rao, G.N.V. (2021). Ensemble Methods for Scientific Data—A Comparative Study. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_51
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