| Parkinson’s Detection System Using Machine Learning Algorithm | |
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MSRDG International Journal of Computer Scientific Technology & Electronics Engineering
© 2025 by MSRDG IJCSTEE Journal Volume 1 Issue 1 Year of Publication: 2025 |
Paper Download Article ID MSRDG-IJCSTEE-V1I1P104 |
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Abstract: Parkinson's disease (PD) is a “neurodegenerative disorder” that affects millions of people worldwide. In recent years, there has been a growing interest in developing computer-based Parkinson's disease detection systems that can provide accurate and non- invasive diagnostic methods. The Parkinson's detection system utilizes various techniques and technologies such as machine learning algorithms, signal processing, and wearable sensors to detect the disease at an early stage. The system works by analyzing the patient's motor symptoms, speech patterns, and other physiological parameters to identify the presence of PD. Machine learning algorithms such as “decision trees, support vector machines, and artificial neural networks” are commonly used to analyze large amounts of data and identify patterns in the patient's symptoms. Signal processing techniques such as Fourier transforms and wavelet transforms are used to extract relevant features from the patient's movement and speech data. The use of wearable sensors such as accelerometers, gyroscopes, and microphones enable the collection of continuous and objective data from the patient's daily activities. The data collected from these sensors are used to monitor the progression of the disease and adjust the treatment accordingly. In conclusion, the PD system has the potential to revolutionize the early detection and diagnosis of PD. The system can provide accurate and non-invasive diagnostic methods that can improve the quality of life of PD patients. Further research and development in this field can lead to more effective treatments and management strategies for Parkinson's disease. |
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| Keywords: PD, Speech, Symptoms, Machine Learning Algorithms, Data, Sensors | |
