: Hearing loss or hearing impairment is one of the most leading cause highly affecting the people around the world in present days. Also, the youngsters and adults are mostly affecting by this disease, which affects their normal/social life, carrier, education, and etc. Hence, it should be properly identified and diagnosed for providing an earlier treatments to save the people live. For this purpose, the different types of automated hearing loss prediction/detection systems are developed in the conventional works. The existing works are mainly focused on deploying the machine learning/deep learning based prediction approaches for disease identification. However, it lacks with the flaws of difficult computational steps, more training & testing time, increased mis-prediction results, and error outputs. Therefore, the proposed work objects to develop a Human Age - Hearing Impairment & Level (HAHIL) prediction system by using the machine learning methodologies. Here, three distinct prediction models are deployed for age prediction, hearing loss detection, and its severity level prediction. The Biased Probability Neural Network (BPNN) technique is utilized to predict the age based on simulated human acoustical signals. Then, the Regularized Extreme Learning Machine (RELM) mechanism is deployed for predicting the hearing impairment by constructing the weight and target matrices. During evaluation, the performance of the proposed HAHIL prediction system is validated and tested by using various evaluation indicators.

The creation of an automated system for heart disease detection was once one of the more common undertakings in the healthcare industries. For this purpose, the different types of big data analytics technologies are developed in the conventional works to predict the heart disease. Still, it limits with the problems associated to the elements of high complexity, time consumption, over fitting, and mis-prediction results. Because the previous methods did not optimize the best features, they did not give accurate results in heart attack detection, so the system is needed to control the death ratio.Therefore, the proposed work objects to implement a novel Mine Blast Optimization (MBO) based Multi-Layer Perceptron Neural Network (MLPNN) technique to predict the heart attack from the given datasets. The proposed detection framework includes the stages of preprocessing, feature optimization, and classification. Here, the regression based preprocessing model is implemented to normalize the attributes for increasing the quality. Then, the MBO technique is also used to choose the relevant features based on the best optimal solution. It also helps to reduce the increase the training of classifier with reduced time consumption and high detection accuracy. Finally, the MLPNN technique is utilized to predict the classified label as whether normal or disease affected. During analysis, the results of the proposed MBO-MLPNN technique is validated and compared by using various measures. Here the proposed method achieved 98% accuracy performance for heart attack detection than former methods.