ACO Feature selection and Novel Black Widow meta-heuristic Learning rate optimized CNN for Early diagnosis of Parkinson’s disease

Abstract

Parkinson's disease (PD) is a brain cell disease that causes brain cells to interact with a substance called dopamine. The dopamine-producing cells in the brain are answerable for regulating, regulating, and facilitating movement. When 60-80% of these cells are misplaced, dopamine is not formed and Parkinson's motor indicationsseem. The disease is thought to have started years before the onset of motor symptoms. Therefore, investigators are looking for ways to identify non-motorized symptoms that appear at the onset of the disease and avert the development of the disease. In this paper, ACO Feature selection (FS) and Novel Black Widow meta-heuristic Learning rate optimizedConvolutional Neural Network for Early diagnosis of PD is introduced. The proposed diagnostic method includes methods of selection and classification of characteristics. When selecting the characteristics, the meaning of the characteristic and the methods of optimization (OEC) of the ant colony were taken into consideration. The proposed learning rate optimization gives CNN better performance than other methods. Achieved 93.84% accuracy in the diagnosis of PD with minimal noise function.