Abstract Paper

Title	: ALZHEIMER’S ILLNESS PROGNOSIS USING HYBRID GENETIC FEATURE SELECTION AND SUPPORT VECTOR MACHINE CLASSIFICATION
Author(s)	: V. Nandhini, R. Pugazendi
Article Information	: Volume 2 - Issue 2 (December - 2018) , 52-56
Affiliation(s)	: Research Scholar, Dept. of Computer Science, Government Arts College, Periyar University, Salem – 7, Tamilnadu, India.
	: Assistant Professor, Dept. of Computer Science, Government Arts College, Periyar University, Salem – 7, Tamilnadu, India.

---

Abstract :

Alzheimer's Illness (AI) is the general method of mental sickness, enduring a large number of aged individuals around the world. It is an age-related mind disease that gradually endures the individual memory and conceptual abilities and fitness even the aptitudes to misbehave to least demanding exercises. Be that as it may, commonly the memory and remaining idea issues are escalated and are hard to do regular assignment. Alzheimer's illness (AI) is the most stressed activity in the contemporary world their correct estimate can't be made independently. A computerized framework requires to be extended for the estimate for this disease successful and fast method. So, in this paper introduces the AI detection process using hybridized method called genetic feature selection process and support vector machine based classification algorithm.There are distinctive neuropsychological tests, the different calculations utilized with the end goal of conclusion and the apparatus that might be utilized for the investigation to discover Alzheimer's illness. NACC (National Alzheimer's Coordinating Center) having the Researchers information Dictionary – Uniform Data Set(RDD_UDS)is gives dataset for researchers. Thus the work gives better exactness and a superior outcome

---

Keywords	: : Alzheimer’s illness, diagnosis methods, data mining techniques, SVM, Genetic Algorithm, NACC, RDD_UDS.
Document Type	: Research Paper
Publication date	: November 27, 2018

---