PD (Parkinson’s disease) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. It is associated with progressive neuronal loss in the substantial nigra and other brain structures. Non-motor features, such as dementia and dysautonomia, occur frequently, especially in advanced stages of the disease. Diagnosis depends on the presence of two or more cardinal motor features such as rest tremor, bradykinesia, or rigidity. Functional neuroimaging holds the promise of improved diagnosis and allows assessment in early disease. In the normal brain, some nerve cells produce the chemical dopamine, which transmits signals within the brain to produce smooth movement of muscles. In Parkinson’s patients, 80 percent or more of these dopamine-producing cells are damaged, dead, or otherwise degenerated. This causes the nerve cells to fire wildly, leaving patients unable to control their movements. Though full-blown Parkinson’s can be crippling or disabling, experts say early symptoms of the disease may be so subtle and gradual that patients sometimes ignore them or attribute them to the effects of aging. At first, patients may feel overly tired, “down in the dumps,” or a little shaky. Their speech may become soft and they may become irritable for no reason. Nowadays, with the development of computer technology, data mining has been widely used in various fields. Clinical decision support systems (CDSSs) form a significant part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, from diagnosis and investigation through treatment and long-term care. Parkinson’s disease classification is proposed with enhanced soft computing techniques. Effective prediction of status of a patient is carried out using statistical feature extraction and neural network classifier. EMG signals with movement disorders are considered for classification. Parkinson’s disease classification inaccuracy is the main problem here. Though the existing system provides 80% accuracy, the classification fails to provide result for unlabeled data.