Browsing by Author "Hassan, Md. Redone"

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A Knowledge Base Data Mining Based on Parkinson's Disease
(IEEE, 2019-11) Hassan, Md. Redone; Kadir, S.K. Obidul; Islam, Md. Aminul; Abujar, Sheikh; Zannat, Raihana; Ohidujjaman
The approaches to detecting Parkinson's disease in the human body from voice data by using Classification techniques apply three different algorithms for finding the growth rate of this disease. Unified Parkinson's disease rating scale deals with motor fluctuations and changes over voice after a certain period and that can measure the people affected by this disease and the difference with healthy people. Hoehn & Yahr scale measures the symptoms which are being working through the improvement of Parkinson's disease in the human body. Classifier algorithms used to detect the factors and symptoms which are involved in the advancement of this disease in the human body using voice data. From the distinctions of all algorithms measures the growth rate and find out which algorithm gives the best result for several approaches to diagnosis Parkinson's disease and chances of had this disease in the human body.
‘A knowledge Base Data Mining Based on Parkinson's Disease
(Daffodil International University, 2019-04-25) Hassan, Md. Redone; Aminul Islam, Md.; Obidul Kadir, S.K.
The approaches of detecting Parkinson’s disease (PD) in human body from voice data using Classification techniques apply three different algorithms for finding the growth rate of Parkinson’s disease. Support vector machine (SVM), K-nearest neighbor (K-NN) and Decision tree (DT) are applied to detect the progression rate of Parkinson’s disease (PD) by using Unified Parkinson’s disease rating scale (UPDRS) and Hoehn &Yahr scale (HYS) . Unified Parkinson’s disease rating scale (UPDRS) deals with motor fluctuations and change over voice after certain period and that can measure the people affected by Parkinson’s disease and healthy people. Hoehn & Yahr scale (HYS) are measures the symptoms which are related to progression of Parkinson’s disease (PD) in human body. Classifier algorithms used to detect the factors and symptoms which are related to progression of Parkinson’s disease (PD) in human body using voice data. The algorithms can detect Parkinson’s disease (PD) by several approaches which criteria are related to progression of Parkinson’s disease (PD) in human body. From the distinctions of all algorithms measures which algorithm give the best accuracy for several approaches to diagnosis Parkinson’s disease (PD) and risk factors of had Parkinson’s disease (PD) in human body.
A Knowledge Based Data Mining Based on Parkinson’s Desease
(Daffodil International University, 2019) Hassan, Md. Redone; Islam, Md. Aminul; Obidul Kadir, S.K.
The approaches of detecting Parkinson’s disease (PD) in human body from voice data using Classification techniques apply three different algorithms for finding the growth rate of Parkinson’s disease. Support vector machine (SVM), K-nearest neighbor (K-NN) and Decision tree (DT) are applied to detect the progression rate of Parkinson’s disease (PD) by using Unified Parkinson’s disease rating scale (UPDRS) and Hoehn &Yahr scale (HYS) . Unified Parkinson’s disease rating scale (UPDRS) deals with motor fluctuations and change over voice after certain period and that can measure the people affected by Parkinson’s disease and healthy people. Hoehn & Yahr scale (HYS) are measures the symptoms which are related to progression of Parkinson’s disease (PD) in human body. Classifier algorithms used to detect the factors and symptoms which are related to progression of Parkinson’s disease (PD) in human body using voice data. The algorithms can detect Parkinson’s disease (PD) by several approaches which criteria are related to progression of Parkinson’s disease (PD) in human body. From the distinctions of all algorithms measures which algorithm give the best accuracy for several approaches to diagnosis Parkinson’s disease (PD) and risk factors of had Parkinson’s disease (PD) in human body.