Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition

Parkinson’s disease (PD) is a global public health problem of enormous dimension. In this study, we aimed to discriminate between healthy people and people with Parkinson’s disease (PD). Various studies revealed, that voice is one of the earliest indicator of PD, and for that reason, Parkinson dataset that contains biomedical voice of human is used. The main goal of this paper is to automatically detect whether the speech/voice of a person is affected by PD. We examined the performance of fuzzy c-means (FCM) clustering and pattern recognition methods on Parkinson’s disease dataset. The first method has the main aim to distinguish performance between two classes, when trying to differentiate between normal speaking persons and speakers with PD. This method could greatly be improved by classifying data first and then testing new data using these two patterns. Thus, second method used here is pattern recognition. The experimental results have demonstrated that the combination of the fuzzy c-means method and pattern recognition obtained promising results for the classification of PD

Diagnosis of Parkinson’s Disease using Principal Component Analysis and Boosting Committee Machines

Parkinson’s disease (PD) has become one of the most common degenerative disorders of the central nervous system. In this study, our main goal was to discriminate between healthy people and people with Parkinson’s disease. In order to achieve this we used artificial neural networks, and dataset taken from University of California, Irvine machine learning database, having 48 normal and 147 PD cases. We examine the performance of neural network systems with back propagation together with a majority voting scheme. In order to train examples we used boosting by filtering technique with seven committee machines, and principal component analysis is used for data reduction. The experimental results have demonstrated that the combination of these proposed methods has obtained very good results with correct positive value of 92% on the classification of PD.

Diagnosis of Cardiovascular Diseases by Boosted Neural Networks

A boosting by filtering technique for neural network systems with back propagation together with a majority voting scheme is presented in this paper. Previous research with regards to predict the presence of cardiovascular diseases has shown accuracy rates up to 72.9%. Using a boosting by filtering technique prediction accuracy increased over 80%. The designed neural network system in this article presents a significant increase of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to > 90 in the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data set

Clinical Diagnosis of Various Cancer Patients using PET/CT Fusion Imaging Techniques

Many types of cancers begin because of out of control growth of abnormal body cells and it is named for the site where it get started and spread. Growing rate of these cells is different and they are diagnosed as well as treated differently. Positron Emission Tomography (PET-Radio tracer) and Computed Tomography (CT) are two different diagnostic modalities in which PET provides, functional and physiological computer generated images obtained from a detection of photon annihilated radionuclide 18F-FDG (Fluro deoxy glucose- radioactive tracer), i.e., distributed in tissues, decays in the body to release positrons where as CT provides morphological, anatomical information that uses X-ray beam which helps in determing site with extent of malignancies. Thus, combined PET/CT modality provides metabolic functions and anatomical functions in single detection of whole body. This paper describes various image processing application techniques that are introduced and developed to minimize artifacts and enhance the required feathers for correct diagnosing which will help the radiologist as well as the physician to decide the way of treatment with medications for curing cancer patients

A New Data Mining Scheme Using Artificial Neural Networks

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems

ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF PANCREATIC CANCER

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