Dew computing-inspired health-meteorological factor analysis for early prediction of bronchial asthma

Bronchial asthma is one of the most common chronic diseases of childhood and considered as a major health problem globally. The irregularity in meteorological factors has become a primary cause of health severity for the individuals suffering from asthma. In the presented research, a dew-cloud assisted cyber-physical system (CPS) is proposed to analyze the correlation between the meteorological and health parameters of the individuals. The work is primarily focused on determining the health adversity caused by the irregular scale of meteorological factors in real-time. IoT-assisted smart sensors are utilized to capture ubiquitous information from indoor environment that make a vital impact on the health of the individual directly or indirectly. The data is analyzed over the cyber-space to quantify the probable irregular health events by utilizing the data classification efficiency of Weighted-Na?ve Bayes modeling technique. Moreover, the relationship between meteorological and health parameters is estimated by utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and calculate a unifying factor over the temporal scale. To validate the monitoring performance, the proposed model is implemented in the four schools of Jalandhar, India. The experimental evaluation of the proposed model acknowledges the performance efficiency through several statistical approaches. Furthermore, the comparative analysis is evalu-ated with state-of-the-art decision-making algorithms that demonstrate the effectiveness of the proposed solution for the targeted application.