Paper Title
Essential Tremor Detection using Naive Bayes classifier

Millions of people living their lives with shaky hands and are unable to perform their daily chores; it’s the tremor (muscular disorder) that is paralyzing their lives. Tremor is a rhythmic unintentional muscular movement that causes to and fro motion of the particular part of a body which includes hand, arm, face, voice, trunk, head, legs, etc. Hand tremor is most common and is usually because of the side effect of certain drugs. It usually gets worst with the passage of time. It mostly affects older adults, but young people are also the victim of it, so its identification is essential so that the patient can get the required treatment. This paper includes the identification of tremor using a Naive Bayes classifier. The classifier will not only detect the tremor but will also identify its key features which include amplitude and its frequency which will help the doctor to provide the required treatment to the patient. The results showed that classifier is pretty convincing with the success rate of 60% to 70%, but we can achieve higher success rate merely by increasing the number of samples and features.