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  • Open Access


    Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624

    Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated… More >

  • Open Access


    Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

    E. Bharath1,*, T. Rajagopalan2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102

    Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy… More >

  • Open Access


    Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

    Sathya Bama B*, Bevish Jinila Y

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481

    Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson’s… More >

  • Open Access


    Effects of Health Qigong Exercise on Depression and Anxiety in Patients with Parkinson’s Disease

    Xiying Li1, Alyx Taylor2, Jinming Li3, Ting Wang3, Jing Kuang3, Zhihao Zhang3, Xiaolei Liu4, Tingting Liu4, Xia Qin5, Shenghua Lu6,7,*, Liye Zou3

    International Journal of Mental Health Promotion, Vol.24, No.6, pp. 855-867, 2022, DOI:10.32604/ijmhp.2022.021508

    Abstract Objective: This study explored the effects of Health Qigong exercise on depression and anxiety in patients with Parkinson’s disease (PD). Methods: A total of 42 volunteers who met the inclusion criteria were recruited and randomly allocated into the experimental group and the control group. The experimental group carried out 60-minute sessions of Health Qigong exercise five times a week for 12 weeks while the control group did not perform any regular physical exercise. Data on cognitive impairment, psychomotor retardation, somatic anxiety, weight loss and sleep disorders, the sum score of the 17-item Hamilton Depression Rating Scale (HDRS-17), state anxiety, trait… More >

  • Open Access


    Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

    C. Santhosh Kumar1, K. Vishnu Kumar2,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669

    Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading… More >

  • Open Access


    Pharmacotherapeutics and molecular docking studies of alpha-synuclein modulators as promising therapeutics for Parkinson’s disease


    BIOCELL, Vol.46, No.12, pp. 2681-2694, 2022, DOI:10.32604/biocell.2022.021224

    Abstract Parkinson’s disease (PD) is an age-related neurodegenerative ailment that affects dopamine-producing neurons in a specific area of the brain called the substantia nigra of the ventral midbrain. It is clinically characterized by movement disorder and marked with unusual synaptic protein alpha-synuclein accumulation in the brain. To date, only a few Food and Drug Administration (FDA) approved drugs are available on the market for the treatment of PD. Nonetheless, these drugs show parasympathomimetic related adverse events and remarkably higher toxicity; hence, it is important to find more efficacious molecules to treat PD. In our study, We chosen 22 natural compounds as… More >

  • Open Access


    Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

    Ashit Kumar Dutta1,*, Nazik M. A. Zakari2, Yasser Albagory3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2195-2207, 2023, DOI:10.32604/csse.2023.026461

    Abstract Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify… More >

  • Open Access


    Detection of Parkinson’s Disease with Multiple Feature Extraction Models and Darknet CNN Classification

    G. Prema Arokia Mary1,*, N. Suganthi2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 333-345, 2022, DOI:10.32604/csse.2022.021164

    Abstract Parkinson’s disease (PD) is a neurodegenerative disease in the central nervous system. Recently, more researches have been conducted in the determination of PD prediction which is really a challenging task. Due to the disorders in the central nervous system, the syndromes like off sleep, speech disorders, olfactory and autonomic dysfunction, sensory disorder symptoms will occur. The earliest diagnosing of PD is very challenging among the doctors community. There are techniques that are available in order to predict PD using symptoms and disorder measurement. It helps to save a million lives of future by early prediction. In this article, the early… More >

  • Open Access


    Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

    Omar M. El-Habbak1, Abdelrahman M. Abdelalim1, Nour H. Mohamed1, Habiba M. Abd-Elaty1, Mostafa A. Hammouda1, Yasmeen Y. Mohamed1, Mohanad A. Taifor1, Ali W. Mohamed2,3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109

    Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by… More >

  • Open Access


    Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network

    Rajalakshmi Shenbaga Moorthy1,*, P. Pabitha2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3101-3119, 2021, DOI:10.32604/cmc.2021.016489

    Abstract Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression. This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network (IRBFNN). Particle swarm optimization (PSO) with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN. The performance of RBFNN is seriously affected by the centers of hidden neurons. Conventionally K-means was used to find the centers of hidden neurons. The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.… More >

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