Analysis and Modeling of Contextual Activity Pattern for Identifying Lifestyle Disorders

Nowadays with the help of various government health records and surveys, it has been found that the root cause of the various severe diseases (non-communicable) very frequently occur in the early age of humans and adolescents has not even aware of any symptoms or signs. This type of disease has a root cause of human irregular and very unhealthy, unorganized lifestyle and diet pattern. From the data analysis done on health information collected by various government organizations, it has been found that 309889 adolescents between ages 12-18 (53.8 female genders) from approx. 70 countries have been suffering from this non-communicable disease. Medical departments or health care organizations are producing a very wide amount of health-based data every day which be used for identifying or recognizing or predicting this disease which has no early signs or symptoms during some of its growing stages. This hidden data of healthcare organizations will be useful for taking decisions effectively for affected person treatment and recovery procedure. Here the main challenge is how to extract the hidden data from the collected health records, there some sort of data mining and machine learning technique is required. The expected outcome of our research work is that if the disease is detected and identified at its early stage it can be cured very effectively and easily with more accuracy and thus reducing the risk factor of life of a patient and the cost of treatment required to cure the disease. Early recognition of disease will also increase the percentage rate of cure from a disease of a patient and also decreases the risk of loss of life of a patient. This research paper provides a brief introduction of lifestyle Health problems and its cause. Our proposed research work includes a review of various techniques used by different research scholars, a working block diagram with a brief description of the diagram, and at the end, we have concluded with future work and the scope of our research work. This research can be very helpful for society as an early prediction of diseases can reduce the death rates of the patient.