CLASSIFICATION ON DEGREE OF HARMING IN PARKINSON DISEASE

Baljinder Kaur, Himanshu Aggarwal

Abstract


Machine learning is set of methods which can detect pattern in data, create new pattern, and can estimate future data. There are mainly two types of machine learning supervised and unsupervised. Neural network is technique of machine learning which is used for perposed work. As we have seen that nowadays many neurological disorder problems found in every age group. From these neurological disorders Parkinson disease is gradually increasing in older people. It has generally seen after the age of 50. Symptoms of Parkinson disease seems similar to other neurological disorders at the first time, thats why sometime it is difficult to differentiate it from other neurological disorders. This disease reduce the quantity of dopamine in the substantia nigra,in the part of brain. Dopamine helps to carry signals from brain to all parts of body. When degree of dopamine decreases in brain body movements becomes slow. Its symptoms are generally muscle stiffness, shaking, lower balance of body, and voice distortion. We choose the voice distortion for our research because if person is affected with PD it can be easily identified from its vocal speech. Parkinson disease patient’s voice would be harsh,rigid and sometimes he cannot pronounce words properly. There are many diagnosis system have been developed for Parkinson disease but these systems can only classify it into two stages healthy or unhealthy. So we have tried to classify it into more then two stages so that diagnosis can be more easy. We classify it into 7 stages which shows the degree of harming from his speech signals. We use ANN based stage classifier, we calculate the features then perform pre-processing then classify it. Our result for this research work is shows 88.9% accuracy in true classification of data in seven different stages of the disease.

Keywords


Parkinson disease, Stage classification, PD classification through voice

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DOI: https://doi.org/10.26483/ijarcs.v8i8.4668

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