PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH

Source abstract

Motor symptoms, such as tremors, bradykinesia, stiffness, and posture issues, are produced by the loss of dopamine-producing neurons in the spinal column portion of the brain, which is characteristic of Parkinson's disease (PD). To properly control and treat PD, the condition must be identified as soon as possible. Machine learning techniques, which use data-driven methodologies, provide intriguing possibilities for reaching this aim. These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. These models support early diagnosis and enable personalized treatment strategies tailored to the specific needs of patients. Additionally, integrating wearable sensors and mobile health technologies further enhances the feasibility of continuous monitoring and early detection, providing patients and healthcare practitioners with the tools they need to manage PD proactively. To identify diseases, one can access vast databases of medical information. To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. The proposed research applies a few Machine Learning ways to anticipate Parkinson's disease by human guidance, with the dataset acting as the source of the process understanding. By applying the hyperparameter optimization process, the accuracy is estimated. When used to diagnose Parkinson's disease (PD), the proposed methods produce accuracy rates of 98.9% for Naive Bayes and 97.3% for Logistic Regression.