An Improvised Technique for the Diagnosis of Asthma Disease with the Categorization of Asthma Disease Level

The functioning of the lung tissues will be affected by asthma; the proper treatment needs to be given to the asthma patients to ensure the human’s safety. In the earlier research work, vote-based ensemble classifier approach is utilized for disease diagnosis. Nevertheless, the level of the disease may differ for every patient in terms of different factors like age and environmental situations which might affect the proper treatment. This problem is resolved by presenting the asthma disease finding and level categorization technique (ADF-LCT) which is utilized to detect the various categories of asthma disease level in terms of patient’s health status. In the proposed work, the Bayesian network is utilized to detect the existence of the disease by calculating the probability difference among the asthma genome profile and the input gnome details. Then, the disease level is detected by classifying patient’s health details into three main categories such as low severe asthma (LSA), middle severe asthma (MSA), high severe asthma (HSA), and very high severe asthma (VHSA). The overall research of the work is executed in MATLAB simulation environment by utilizing the genome expression which proved proposed work leads to efficient prediction outcome.