Parkinson disease prediction using Regression technique in python

 

Parkinson disease prediction using Regression technique

 

 PROJECT ID: PYTHON19

 

PROJECT NAME: Parkinson disease prediction using Regression technique

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Parkinson’s disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson’s datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Related Work

For effective diagnosis of Parkinson’s Disease (PD), different types of classification methods were examined by Das30. The computation of the performance score of the classifiers was based on various evaluation methods. According to the results of application scores, they found that Neural Networks (NNs) classifier obtains the best result which was 92.9% of accuracy. Bhattacharya and Bhatia31 used data mining tool, Weka, to pre-process the dataset on which they used Support Vector Machine (SVM) to distinguish people with PD from the healthy people. They applied LIBSVM to find the best possible accuracy on different kernel values for the experimental dataset. They measured the accuracy of models using Receiver Operating Characteristic (ROC) curve variation. Chen et al.13 presented a diagnosis PD system by using Fuzzy K-Nearest Neighbor (FKNN). They compared the results of developed FKNN-based system with the results of SVM based approaches. They also employed PCA to further improve the PD diagnosis accuracy. Using a 10-fold cross-validation, the experimental results demonstrated that the FKNN-based system significantly improve the classification accuracy (96.07%) and outperforms SVM-based approaches and other methods in the literature. Ozcift32 developed a classification method based on SVM and obtained about 97% accuracy for the prediction of PD progression. Polat29 examined the Fuzzy C-Means (FCM) Clustering-based Feature Weighting (FCMFW) for the detection of PD. The author used K-NN classifier for classification purpose and applied it on the experimental dataset with different values of k. Åström and Koker33 proposed a prediction system that is based on parallel NNs. The output of each NN was evaluated by using a rule-based system for the final decision. The experiments on the proposed method showed that a set of nine parallel NNs yielded an improvement of 8.4% on the prediction of PD compared to a single unique network. Li et al.34 proposed a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, they applied Principal Component Analysis (PCA) to extract the optimal subset of features and SVM for predicting PD. Guo et al.35 developed a hybrid system using Expectation Maximization (EM) and Genetic Programming (GP) to construct learning feature functions from the features of voice in PD context. Using projection based learning for meta-cognitive Radial Basis Function Network (PBL-McRBFN), Babu and Suresh (2013) implemented a gene expression based method for the prediction of PD progression. The capabilities of the Random Forest algorithm was tested by Peterek et al.36 for the prediction of PD progression. A hybrid intelligent system was proposed by Hariharan et al.24 using clustering (Gaussian mixture model), feature reduction and classification methods. Froelich et al.23 investigated the diagnosis of PD on the basis of characteristic features of a person’s voice. They classified individual voice samples to a sick or to a healthy person using decision trees. Then they used the threshold-based method for the final diagnosis of a person thorough previously classified voice samples. The value of the threshold determines the minimal number of individual voice samples (indicating the disease) that is required for the reliable diagnosis of a sick person. Using real-world data, the achievement of accuracy of classification was 90%. Eskidere et al.25 studied the performance of SVM, Least Square SVM (LS-SVM), Multilayer Perceptron NN (MLPNN), and General Regression NN (GRNN) regression methods to remote tracking of PD progression. Results of their study demonstrated that the best accuracy is obtained by LS-SVM in relation to the other three methods, and outperforms latest proposed regression methods published in the literature. In a study by Guo et al.10 in Central South of Mainland China, sixteen Single-Nucleotide Polymorphisms (SNPs) located in the 8 genes and/or loci (SNCA, LRRK2, MAPT, GBA, HLA-DR, BST1, PARK16, and PARK17) were analysed in a cohort of 1061 PD, and 1066 Normal healthy participants. This study established that Rep1, rs356165, and rs11931074 in SNCA gene, G2385R in LRRK2 gene, rs4698412 in BST1 gene, rs1564282 in PARK17, and L444P in GBA gene have an independent and combined significant effect on PD. As a final point, this study has reported that SNPs in these 4 genes have more pronounced effect on PD.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

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