Published Paper Details:

In contemporary world, a man or women is engaged with different worldly duties in their profession, work, commitments, family maintenance etc. Smart medicine has emerged to contribute the evolution of healthcare and medical services by applying machine learning together with advanced computing techniques like cloud computing to computer-aided diagnosis and treatments. Depression is another name for mental illness. It can lead to severe health complications and increase the risk of stroke, and sometimes death. Machine learning is mainly focusing on Mind and then Matter. Main motive of this project is to give the analysis and prediction of depression because 21% people are affected in this psychological disease across the nation. In this paper, we collected the datasets which contains patient records and histories with medication process. We analyzed the hypothesis from the records, to point out which are all the major factors to affect the man or women. Besides we are highlighting the factors with grades in different geographical structures like Family problem, Physical illness, Lust or desire not obtained so far. We are introducing ensemble learning method for predicting the disease level for the particular node. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. We compute a unified medicine framework based on the depression identification parameters using