Disease Recognition using Machine Learning Techniques
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Healthcare is very important aspect in everyone's life. Elder people are usually dependent on medicines and continuous health support from outside environment for their health management. The bones of people in age group 65 and above have become so weak that they are not able to walk within their homes, so, they prefer to stay alone rather than being shifted to old age homes or nursing care centers as it gives a feeling of independence to them. To prevent the late recognition and treatment of health problems, systems for automatic health monitoring, diagnose and care have been proposed. Once t...
Healthcare is very important aspect in everyone's life. Elder people are usually dependent on medicines and continuous health support from outside environment for their health management. The bones of people in age group 65 and above have become so weak that they are not able to walk within their homes, so, they prefer to stay alone rather than being shifted to old age homes or nursing care centers as it gives a feeling of independence to them. To prevent the late recognition and treatment of health problems, systems for automatic health monitoring, diagnose and care have been proposed. Once the system identifies a problem, the physician is notified with the details of the problem diagnosed. The movement pattern of people is acquired using a motion capture system and then classified into: Parkinson disease, Back pain, Normal, Hemiplegia and leg pain. The various classification techniques are applied on the dataset and the clustering is done. Validity of the clustering algorithm is done using CVAP. The classification accuracies of various classification techniques are compared and tested.
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