Parkinson's disease (PD), or simply Parkinson's, is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms usually emerge slowly, and as the disease worsens, non-motor symptoms become more common. The most obvious early symptoms are tremor, rigidity, slowness of movement, and difficulty with walking. Cognitive and behavioral problems may also occur with depression, anxiety, and apathy occurring in many people with PD.
The cause of PD is unknown, with both inherited and environmental factors believed to play a role. Those with an affected family member are at an increased risk of getting the disease, with certain genes known to be inheritable risk factors. Other risk factors are those who have been exposed to certain pesticides and who have prior head injuries. Coffee drinkers, tea drinkers, and tobacco smokers are at a reduced risk.
Diagnosis of typical cases is mainly based on symptoms, with motor symptoms being the chief complaint. Tests such as neuroimaging (magnetic resonance imaging or imaging to look at dopamine neuronal dysfunction known as DaT scan) can be used to help rule out other diseases. Parkinson's disease typically occurs in people over the age of 60, of whom about one percent are affected. Males are more often affected than females at a ratio of around 3:2. When it is seen in people before the age of 50, it is called early-onset PD. By 2015, PD affected 6.2 million people and resulted in about 117,400 deaths globally. The average life expectancy following diagnosis is between 7 and 15 years.
Data Set Characteristics: Multivariate
Number of Instances: 197
Area: Life
Attribute Characteristics: Real
Number of Attributes: 23
Date Donated: 2008-06-26
Associated Tasks: Classification
Missing Values? N/A
The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders.
I have made a Machine Learning Model using the famous Xgboost Algorithm. This model predicts the possiblity of Parkinsons disease in a person, according to the data.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now.
It is available at: github.com/dmlc/xgboost