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Analysis and Prediction of health issues for teaching profession using Semantic Techniques

The teaching profession is considered as the safest with least physical strain. However teachers also undergo work related illness and health hazards. Most of the existing system discuss the prediction of general or specific health issues of common people regardless of any profession. Also there are very few systems that predicts profession related illness, which focusses mainly on field related issues. The paper proposes analysis and prediction of diseases such as voice disorders, chronic laryngitis and respiratory illness like asthma that affects teachers due to their profession and that can be diagnosed at an initial stage using semantic techniques. The negEx algorithm is used to filter and retrieve negated findings and diseases from patient's medical records. The work also involves semantic techniques like Bio Ontology Annotator, First Order Logic (FOL), Description Logic (DL) to predict the probable diseases with the help of teacher's medical records and their routine lifestyle nature. The proposed method could help teachers in earlier detection of complicated diseases.