Abstract:
Parkinson’s disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson’s disease in the United States whereas the number of people suffering from Parkinson’s disease worldwide is around 5 million. Thus, it is very important for us to predict Parkinson disease in early stages so that we can take the necessary treatment. People are mostly familiar with the motor symptoms of Parkinson’s disease, however an increasing amount of research is being done to predict the Parkinson’s disease from non-motor symptoms that precede the motor ones. If we are able to predict the disease in early stages then we can see to that the patients get a …show more content…
The main cause of Parkinson’s disease is actually unknown. However, it has been researched that the combination of environmental and genetic factors play an important role in causing PD [1]. For general understanding the Parkinson’s disease is treated as disorder of the central nervous system which is the result of loss of cells from various parts of the brain. These cells also include substantia nigra cells that produce dopamine. Dopamine plays a vital role in the coordination of movement. It acts as a chemical messenger for transmitting signals within the brain. Due to the loss of these cells, patients suffer from movement …show more content…
non-motor symptoms and motor symptoms. Many people are aware of the motor symptoms as they can be visually perceived by human beings. These symptoms are also called as cardinal symptoms, these include resting tremor, slowness of movement (bradykinesia), postural instability (balance problems) and rigidity [2]. It is now established that there exists a time-span in which the non-motor symptoms can be observed. This symptoms are called as “dopamine-non-responsive” symptoms. These symptoms include cognitive impairment, sleep difficulties, loss of sense of smell, constipation, speech and swallowing problems, unexplained pains, drooling, constipation and low blood pressure when standing. It must be noted that none of these non-motor symptoms are decisive, however when these features are used along with other biomarkers from Cerebrospinal Fluid measurement (CSF) and dopamine transporter imaging, they may help us to predict PD.
In this paper we extend works by Prashant et al [3]. This work takes into consideration the non-motor symptoms and the biomarkers such as Cerebrospinal Fluid Measurements and dopamine transporter imaging. In this paper we carry out a similar approach, however we try to use different machine learning algorithms that can help in improving the model and also play a vital role in making in early prediction of PD which in turn will help us to initiate neuroprotective therapies at the right