Heart disease is the biggest cause of death in the globe. The method of predicting cardiac disease is exceedingly complex. It can only be done properly if the doctor has a lot of expertise and is well-versed in the condition. IoT-based illness prediction is a relatively recent technology for accurately classifying diseases based on sensor data. This system proposes an enhanced deep learning-based framework for predicting the heart disease. The general publicly available Hungarian heart disease dataset is utilized for the implementation, which includes heart disease related data collected from patients through IoT sensor devices. The input dataset is preprocessed using Median Studentized Residual approach for resolving error data and missing values. Preprocessed data values are feature selected by Harris Hawk Optimization (HHO) approach. The selected features are then classified into normal and abnormal by Modified Deep Long Short-Term Memory (MDLSTM). The modification in LSTM output is altered using Improved Spotted Hyena Optimization (ISHO) algorithm. The results are implemented in the working platform of Phyton with the metrics such as specificity, sensitivity, F-Score, Kappa value, Accuracy, BER and Execution time. The analyzed results shows that the implemented methodology is superior in the prediction of heart disease with an accuracy of 98.01% and reduced error rate of 91.11 compared with other existing techniques.

Rice plays a critical role in global food security as a staple food for over half the world’s population,. However, rice crops are vulnerable to diseases that c Rice plays a critical role in global food security as a staple food for over half the world’s population,. However, rice crops are vulnerable to diseases that c

Diabetic Retinopathy (DR) is a medical condition that arises when there is leakage of blood and other fluids from the tiny retinal blood vessels. If it is left Diabetic Retinopathy (DR) is a medical condition that arises when there is leakage of blood and other fluids from the tiny retinal blood vessels. If it is left

Personal recognition can be effectively done using Palm print recognition technology. A new biometric approach using palm print technology for online personal i Personal recognition can be effectively done using Palm print recognition technology. A new biometric approach using palm print technology for online personal i