Abstract- The three deep learning classification models, namely Convolutional Neural Network (CNN), Recurrent NeuralNetwork (RNN), and Multilayer Perceptron (MLP) are tested to forecast human diseases based on their symptoms in thisstudy. A publicly available dataset that was gained in Kaggle was involved, and the data were readily used in the classificationmodels without some further manipulations or feature selections. All the models have been trained on 80:20 training testingsplit to provide equal comparison. The standard evaluation measures such as accuracy, precision, recall, and F1-score wereused to evaluate the performance of the classifiers. The experimental findings prove that CNN and RNN models are betterthan the MLP model in their ability to classify and generalize. The tested models had the best accuracy of CNN with 85.63 andthen closely the RNN model with 85.16. The results substantiate the usefulness of deep learning methods in prediction ofdiseases and emphasize their possible use as a part of smart healthcare.