Im having project phase-1 so can any one tell me like what possible qsn they may ask based on our project(our project is alzeimers disease using Machine Learning SVM algorithm)
Answers
Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623.[13] In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner.[14] Leibniz may be considered the first computer scientist and information theorist, for, among other reasons, documenting the binary number system. #my opinion
Alzheimer’s disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer’s causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods.
1. Introduction
Alzheimer’s disease (AD) is the most familiar cause of dementia, with patients comprising 50%–80% of all dementia sufferers. The disease affects memory, cognition, and behavior. As AD is a neurodegenerative condition, several types of atrophy occur in the hippocampus and other areas of the brain. Despite being the 6th leading cause of death in the USA, it is not a common disease. Currently, there is no cure; however, some Machines (SVMs) and neural network classifiers. Extracting essential discriminatory features from MRI brain images is imperative for competent analysis of disease diagnosis. The preferred feature extraction methods, amongst those most frequently used, are independent component analysis [10], wavelet transform [11], and Fourier transform [12]. This study has been conducted using discrete wavelet features and the k-nearest neighbor algorithm (k-NN) [11] on an artificial neural network (ANN) [11, 13]. Zhang and Wang [14] ran AD prediction models using displacement field estimation between AD and healthy controls using an SVM, twin support vector machine (TWSVM), and generalized eigenvalue proximal SVM (GEPSVM) as classifiers. Tomar and Agarwal [15] reviewed several types of twin SVM algorithms, their optimization problems,.
2. Material and Methods
A total of 172 subjects from the ADNI dataset were used—86 AD and 86 HC. In addition, we used 95 subjects from the OASIS dataset—44 HC and 51 subjects suffering from very mild to mild AD.
2.1. Overview of Experimental Data
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI is to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early-onset Alzheimer’s disease AD. For up-to-date information, vis
The OASIS dataset consists of 416 subjects aged between 18 and 96 years. Our study included 51 AD patients (35 with CDR = 0.5 and 16 with CDR = 1) out of 100 having dementia and 44 HC out of 98 normal subjects. Table 2 shows the demographic details of the subjects used in our study. Both men and women are included and all subjects are right handed.