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RISS 인기검색어
- 검색키워드
- 저자 : Surendran D. (검색결과 2 건)
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Rohini M.,Surendran D.,Manoj S. Oswalt 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
Alzheimer's disease (AD), cerebrovascular disease, Lewy-body disease, and Frontal–temporal degeneration disease are the age-related cognitive impairments that cause dementia. However, AD is the primary cause of dementia that causes brain cell degeneration in the geriatric community. Brain cell degeneration is the crucial cause of AD, due to the abnormal accumulation of indissoluble clumps known as plaques and tangles in the human brain's neurons. Amyloid precursor protein levels and Apolipoprotein -E gene are the biomarkers of AD since it causes accumulations and hence blocks the neuron transport system throughout the body. The early onset of AD includes mild-cognitive impairment (MCI) that progresses to complete dementia. Many related works include AD prediction using clinical modality images and cognitive assessments scores of the individuals but have not addressed comparative genome study for signifi cant subjects. However, there is a lack of aff ordable biomarkers for the eff ective early detection of high-risk individuals. In this study, we utilize one or more features of Magnetic Resonance Imaging (MRI) tests and Apolipoprotein-E genotype sequence that provides more signifi cant biomarkers for the early prediction. The ML classifi ers including Support vector classifi er, Gaussian process, AdaBoost, Random Forest, Decision trees learns the subset of patterns that predicts the AD with gene descriptors from microRNA expression profi le and the profi led gene pattern. These signifi cant multiple gene descriptors provide a supportive prediction methodology that apply genotype strength with the ensemble classifi ers. The fi nal optimal model is given by validation evaluations. The support vector classifi er and Random Forest classifi ers had given consistent results for disease conversion and progression from MRI attributes and had given promising results with the validation that showed accuracy greater than 80% and F1 weighted score of 0.8 in disease classifi cation and prognosis. The experimental results had proven 95% accuracy in the saliency values of APOE isoforms implemented in DragonNN framework that will vary AD pathogenic. Hence particular focus and clinical interventions can be given on Aβ genome dependent subjects that predicts the disease
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Edison Prabhu K,Surendran D Electronics and Telecommunications Research Instit 2023 ETRI Journal Vol.45 No.4
Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.
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