Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Neurodegenerative diseases drastically affect human beings without distinction; it does not matter if they are male or female. Sometimes, it is not clear why a person in their life developed a well-known disease in the world such as Parkinson’s disease (PD). Nowadays, various novel machine learning-based algorithms for evaluating Parkinson’s disease have been designed. The most recent strategy, which was developed using deep learning and can forecast the severity of Parkinson’s disease, is the one described here. To identify this disease, a thorough medical history, previous treatment history, physical examinations, and some blood tests and brain films must be completed. Diagnoses are more critical since they are less expensive and less time-consuming. Voice data from 253 people used in the current study corroborates the doctor’s diagnosis of Parkinson’s disease. To acquire the best results from the data, preprocessing is done. To perform the balancing procedure, a systematic sampling strategy was used to select the data that would be analyzed. Several data groups were constructed using a feature selection technique based on the label’s effect strength. Classification algorithms and performance evaluation criteria employ DT, SVM, and kNN. The classification algorithm and data group with the highest performance value were chosen, and the model was created due to this selection. The SVM approach was employed when constructing the model, and 45% of the original data set data were used. The data was sorted from most relevant to least important. 86% performance accuracy was achieved, in addition to excellent results in all other areas of the project. As a result, it has been established that medical decision support will be provided to the doctor with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed.