Evaluation of learning rate training model on heart disease detection using LSTM

Various algorithms have been used to adjust learning rate parameters, but such strategies generally fail to
concentrate on improving the resulting accuracy. Most experts in neural networks use the highest learning rate that allows
fusion. The adjustment was made to the weights, and used different adjustment functions to avoid the impact of improper
parameter adjustment. In this research, two types of optimizers are used, namely SGD Opt and Adam Opt. In conducting
the training and testing process, different learning rate weights are given, namely 0.01, 0.05, and 0.09, with the Adam
optimizer, and the use of the default learning rate, namely the SGD optimizer with the learning rate weight obtained
automatically 0.000000018. In the conducted experiments, the SGD optimizer with ReduceLRonPlateu gets an average
accuracy value of 81% compared to the Adam optimizer, which only gets the highest value of 71% when the learning rate
is 0.05, determined manually. It can be concluded that determining the weight value of the learning rate is risky because if
the weight of the learning rate is small, then the network takes a relatively long time to occupy or reach a convergence
state, even though a small weight will guarantee that the training or testing process will not pass the minimum determination
value (0). In contrast, for large weight, the percentage for loss fluctuations when training will be relatively large, so it is
difficult to reach a convergence state.