The Prediction of Parkinson's Disease Using Regression Analysis

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Table of Contents

  • Abstract
  • Introduction
  • System Analysis
  • Existing System
    Proposed System
  • Objectives:
  • Literature Survey
  • Machine Learning Language
  • Machine Learning Algorithms in Disease
    Prediction Models
    Tree Classifiers
    Logistic Regression (LR)
    Support Vector Machine (SVM )
    System Design
  • Conclusion


The detection of this Parkinson’s disease in past years has been competative work among researchers, because the symptoms of disease came into continuation in middle and ancient years. This disease has a lot of symptoms. But now this article mainly concentrates on the speech articulation difficulty problems of PD affected people and try to overcome the model on the behalf of three data mining techniques. The three data mining techniques are taken from three different domains of data mining i.e. from tree classifier, statistical classifier, and support vector machine. The goal of classifying the Parametric and Non Parametric models is done by the collected dataset of Parkinson’s disease. The Parkinson’s data is mainly tested with two respective models to determine which model provides the peak classification accuracy. In parametric modeling, the Parkinson’s data is classified using Logistic Regression. In Non parametric modeling, the training and test data of Parkinson’s disease is classified using K-Nearest Neighbors and Random Forest Algorithm.

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Parkinson’s disease is a long lasting de-generative disorder of the central nervous system.. The main reason of this disease is generally not known, but we can say it includes with both genetic and environmental factors. The number of cases that occur in India is more than one million per year.

This disease cannot be cured, only treatment can help. Meditation is the remedy which can help to control from getting effected by the symptoms of Parkinson’s disease. The symptoms are most obvious shaking, rigidity, slowness of movement and difficulty in walking which generally come over time. In many people these symptoms take many years to develop, and they remain effected with disease for years. The most famous personalities who have affected from this Parkinson’s disease are, Boxer Muhammad Ali who was diagnosed with this disease in 1984, as the result of undergoing several severe head injuries. Michael J. Fox was diagnosed with this disease in 1991. Adolf Hitler suffered from this disease and his first symptoms were observed was 1937.

The Logistic regression and K nearest neighbor model with six different machine learning algorithms had used to predict the pneumonia mortality. For detection and diagnosis of wide range of biomedical diseases Support vector machines (SVMs) have been used.

System Analysis

Existing System

The Parkinson’s disease symptoms can be classified into two different types i.e. non-motor and motor symptoms. It is now established that the non-motor symptoms can be observed within timespan and this symptoms are called as dopamine-non-responsive symptoms. These symptoms are such as cognitive impairment, sleep difficulties, loss of sense of smell, constipation, speech and swallowing problems, constipation and low blood pressure when standing It must be observed and noted that none of these non-motor symptoms are decisive, and however when these features are also used along with other biomarkers from Cerebrospinal Fluid measurement (CSF) and dopamine transporter imaging, so that may help us to predict the PD.

Proposed System

In the later system this work also takes into consideration the non-motor symptoms and the biomarkers such as cerebrospinal fluid measurements and dopamine transporter imaging. In this paper, however we follow a similar approach, where we try to use different machine learning algorithms that can help in improving the performance of model and also play a significant role in making in early prediction of the disease, later which in turn will help us to start neuroprotective therapies at the correct time.


  • A persisting frustration in the diagnosis, treatment, and research of Parkinson’s disease is the lack of an objective measure of the nigrostriatal dopaminergic deficit.
  • In particular, we need a tool to monitor the progress of the neuronal degeneration.
  • This is very difficult to achieve clinically in Parkinson’s disease because of the complex clinical presentation and the confounding effect of symptomatic therapy.
  • Although PET, with markers of presynaptic dopaminergic function such as 6-fluorodopa, is an accepted measuring tool.

Literature Survey

To compare survival in incident cases of Parkinson disease (PD) with survival in subjects free of PD from the general population. We used the medical records linkage system of the Rochester Epidemiology Project to identify incident cases of PD in Olmsted County, Minnesota, for the period 1976-1995.

The term Shaking Palsy has been vaguely employed by medical writers in general. By some it has been used to designate ordinary cases of Palsy, in which some slight tremblings have occurred:

We examined risk of parkinsonism in occupations (agriculture, education, health care, welding, and mining) and toxicant exposures (solvents and pesticides) putatively associated with parkinsonism. To investigate occupations, specific job tasks, or exposures and risk of parkinsonism and clinical subtypes. Case-control. Eight movement disorders centres in North America.

Neurological therapeutics: principles and practice is a two volume book consisting of 2874 pages by 345 authors. It is divided into 14 system-based sections that are further divided into 271 subject-based chapters. The chapters are generally short and accessible, making this large book surprisingly practical.

Parkinson’s disease (PD) is a degenerative disorder of the central nervous system. It was first described in 1817 by James Parkinson, a British physician who published a paper on what he called ‘the shaking palsy.’ In this paper, he set forth the major symptoms of the disease that would later bear his name. PD belongs to a group of conditions called movement disorders.

Parkinson’s disease (PD) results primarily from the death of dopaminergic neurons in the substantianigra. Current PD medications treat symptoms; none halt or retard dopaminergic neuron degeneration. The main obstacle to developing neuroprotective therapies is a limited understanding of the key molecular events that provoke neurodegeneration. The discovery of PD genes has led to the hypothesis that misfolding of proteins and dysfunction of the ubiquitin- proteasome pathway are pivotal to PD pathogenesis.

Machine Learning Language

Machine Learning Algorithms in Disease

Machine learning algorithms have good history in disease diagnosis and prediction. A large number of papers have been published that exhibited the application of machine learning algorithm in medical field such as diagnosis of disease, prediction of disease, and identification of disease. Initially, three branches of machine learning came into view i.e., symbolic learning, statistical methods and neural networks.

Symbolic learning was described by Hunt, statistical methods described by Nilsson and neural networks by Rosenblatt. Machine learning community has developed large number of machine learning tools that have been widely used to obtain classification models including medical prognostic models. For cancer diagnosis and research, artificial neural network and decision tree classifiers have been used and these methods provided remarkable results.

Logistic regression and K nearest neighbor model with six different machine learning algorithms had used to predict the pneumonia mortality.

Support vector machines (SVMs) have been used foretection and diagnosis of wide range of biomedical diseases such as detection of oral cancers in optical images, polyps in CT colonography.Detection of micro calcifications in mammograms, and analysis of gene expression measured via microarrays.

Prediction Models

In this paper, three different types off classification methods are used i..e decision stump (tree classifiers), logistic regression (statistical classifier) and sequential minimization (support vector machine).

Tree Classifiers

Tree is a classifier that can be defined as a recursive partition of the dataset. Tree classifiers mainly consist a set of nodes in which one of the node acts as root node; all other nodes have exactly one incoming and outgoing edge known as internal nodes and rest of nodes with no outgoing edges known as terminal nodes or leaf nodes.

Logistic Regression (LR)

Few of statistical algorithms are linear discriminate analysis, least mean square quadratic, kernel, logistiregression and k nearest neighbors. But in this paper, Logistic regression is used to obtain desired results. Logistic regression is statistical classifiers that are used for the analysis of data. It can be defined mathematically as Per (G = k | X = x) is a nonlinear function of x and range from 0 to 1 and sum up to 1.

Support Vector Machine (SVM )

Firstly used support vector machines (SVM) to classification purpose. But presently, SVMs have been used in a wide range of problems including pattern recognition bioinformatics and text categorization. Hence, SVM classification has done by realizing a linear or nonlinear separation surface. But it can be found that training of SVM requires solving quadratic optimization problem.

A large number of algorithms are such as the Sequential minimal optimization (SMO), nearest point algorithm (NPA) etc are used to solve this problem. Hence, in this paper, SMO classifier with SVM is used to obtain desired result.

System Design

System Design is the next development stage where the overall architecture of the desired system is decided. The system is organized as a set of sub systems interacting with each other. While designing the system as a set of interacting subsystems, the analyst takes care of specifications as observed in system analysis as well as what is required out of the new system by the end user. As the basic philosophy of Object-Oriented method of system analysis is to perceive the system as a set of interacting objects may also be seen as a set of interacting smaller subsystems that in turn are composed of a set of interacting objects.


In this paper, we try to develop some predication model for Parkinson’s disease identification. For this purpose, three data mining methods i.e., decision stump (tree classifiers), logistic regression (statistical classifiers) and sequential minimization optimization (support vector machine) are used. Dataset that is used in this paper has taken from UCI repository. This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals. PD affected person is represented with a value of 1 and healthy person is represented with 0. To obtain the desired result, 10 cross fold method is used with classifiers as well as three parameters are used to analyze the performance of discussed classifiers.

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