E-Health Parkinson Disease Diagnosis in Smart Home Based on Hybrid Intelligence Optimization Model

The use of internet of things (IoT) in smart home with medical devices within a connected health environment promotes the quick flow of information, the patient’s vital parameters are transmitted by medical devices onto secure cloud based platforms where they are stored, aggregated and analyzed. IoT helps to store data for millions of patients and perform analysis and diagnosis in real-time, promoting an evidence-based medicine system. Different intelligence optimization models can be integrated with IoT to improve the patient healthcare. In this paper, an intelligent optimization model is proposed for monitoring patients with Parkinson’s disease (PD) based on UPDRS assessment (Unified Parkinson’s Disease Rating Scale) from voice records in smart home. Ant lion optimization algorithm (ALO) and adaptive extreme learning machine (ELM) based on differential evaluation (DE) algorithm is proposed; namely (ALO-DEELM), for PD diagnosis. Using this model, home residents will get feedback and keep track on their PD situation. ALO-DEELM model is compared with different machine learning (ML) prediction algorithms and showed the superiority based on different measures. Moreover, the experimental results showed that the proposed model is effective and can significantly reduce the prediction computational time of UPDRS scores. The proposed ALO-DEELM has the potential to be implemented as an intelligent system for PD prediction in healthcare.

[1]  Ahmed M. Anter,et al.  Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation , 2018, J. Comput. Sci..

[2]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[3]  Ahmed M. Anter,et al.  An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural , 2019, Expert Syst. Appl..

[4]  Ke Lu,et al.  Missing data imputation by K nearest neighbours based on grey relational structure and mutual information , 2015, Applied Intelligence.

[5]  Bahram Gharabaghi,et al.  Extreme learning machine model for water network management , 2017, Neural Computing and Applications.

[6]  Mehrbakhsh Nilashi,et al.  Accuracy Improvement for Predicting Parkinson’s Disease Progression , 2016, Scientific Reports.

[7]  H. Gupta,et al.  On typical range, sensitivity, and normalization of Mean Squared Error and Nash‐Sutcliffe Efficiency type metrics , 2011 .

[8]  Alois Krobot,et al.  Musculoskeletal problems as an initial manifestation of Parkinson's disease: A retrospective study , 2012, Journal of the Neurological Sciences.

[9]  Ahmed M. Anter,et al.  Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems , 2019, Soft Computing.

[10]  Mehrbakhsh Nilashi,et al.  A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques , 2017 .

[11]  Max A. Little,et al.  Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests , 2009, IEEE Transactions on Biomedical Engineering.

[12]  Kallol Roy,et al.  Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system , 2019, Energy.

[13]  Tom Kompas,et al.  Determinants of residential water consumption: Evidence and analysis from a 10‐country household survey , 2011 .

[14]  Ravinesh C. Deo,et al.  Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting , 2018, Comput. Electron. Agric..

[15]  Gorkem Serbes,et al.  Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease , 2017, PloS one.

[16]  Y Ben-Shlomo,et al.  How valid is the clinical diagnosis of Parkinson's disease in the community? , 2002, Journal of neurology, neurosurgery, and psychiatry.

[17]  Aboul Ella Hassanien,et al.  Feature Selection Approach Based on Social Spider Algorithm: Case Study on Abdominal CT Liver Tumor , 2015, 2015 Seventh International Conference on Advanced Communication and Networking (ACN).