A DISEASE PREDICTION MODEL USING SPOTTED HYENA SEARCH OPTIMIZATION AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

Early disease prediction is the best solution for any deadly disease in this fast internet world. In this direction, machine learning and deep learning techniques are applied in this fast world to predict diseases early and update the disease level through the Internet of Things techniques. For this purpose, this paper proposes a disease prediction system that uses the newly proposed fuzzy temporal correlation aware classifier and the auto-encoded bidirectional long short-term memory (FTC-Bi-LSTM) to predict the diseases such as heart, cancer, and diabetes. Moreover, this paper proposes a hybrid optimization algorithm called spotted hyena and cuckoo search optimization algorithm (SHCSA) to select the contributed features used to enrich the prediction accuracy. The standard benchmark UCI repository dataset is used to conduct the various experiments and obtain better performance in terms of precision, recall, f-measure, and prediction accuracy. The result is validated by considering the hospital patient data as input and proving the performance.