SRS Diagnosis of Alzheimer’s Disease Using Machine Learning

Alzheimer’s disease is chronic condition that leads to degeneration of brain cells leading at
memory loss. Patients with cognitive mental problems such as confusion and forgetfulness, also other symptoms including behavioral and psychological problems are further suggested having CT, MRI, PET, EEG, and other neuroimaging techniques. The aim of this paper is making use of machine learning algorithms to process this data obtained by neuroimaging technologies for detection of Alzheimer’s in its primitive stage.

Scope of Project Diagnosis of Alzheimer’s Disease Using Machine Learning:

Project scope is the part of project planning that involves determining and documenting a list of specific project  goals, deliverables, tasks, costs and deadlines. Defining the project scope involves adopting a clear vision and an agreement on the outcomes of the project. This allows each milestone of the project to stay on target

Functional and Non Functional Requirements of Diagnosis of Alzheimer’s Disease Using Machine Learning:

Functional Requirements :

A functional requirement shows that what the system must do what services the system present to users. It describes a software system or its component. A function is nothing but inputs to the software system, its behavior, and outputs

Functional Requirements of Diagnosis of Alzheimer’s Disease Using Machine Learning:

1. Image data acquisition and perform preprocessing of the data.
2. Use Brouta Algorithm (available with python package) for feature selection.
3. Apply any suitable machine learning (supervised learning) algorithm for classification.
4. The classification results were calculated by means of three metric measurements, which
are used for quantitative valuation and evaluation, including accuracy, sensitivity (recall)
and specificity. Additionally, numerous optimal approaches such as receiver operating
curve (ROC) and Area under the Curve (AUC) are calculated as well.
5. Use at least two algorithms like support vector machine and Linear Regression and do
comparison to select the best one the basis of performance measures.

NON-FUNCTIONAL REQUIREMENTS Diagnosis of Alzheimer’s Disease Using Machine Learning:

• Application is user friendly.
• Application Perform fast manipulation and calculations.
• Application is adaptable.
• Application will be able to work on all types of operating systems.
• Application will be capable to handle multi user activities simultaneously.
• There will be back up system to face any problem in system
• All the options should be learning friendly I.e. member could easily understand what that option will do if he clicked on it.
• Response Time is very awesome.

Some others are:

• Accessibility
• Maintainability
•  Fault Tolerance.
• Security
• Robustnes

Use Case Diagram of Diagnosis of Alzheimer’s Disease Using Machine Learning

a use case diagram can summarize the details of your system’s users   and their interactions with the system. Scenarios in which your system or application interacts with organizations, people, or external systems. Goals that your system or application helps those entities achieve

Usage Scenarios Diagnosis of Alzheimer’s Disease Using Machine Learning:

A brief user story explaining who is using the system and what they are trying to accomplish. A Scenario is made up of a number of simple, discrete steps that are designated as being performed by either the System or a  User.

ADOPTED METHODOLOGY for Diagnosis of Alzheimer’s Disease Using Machine Learning:

The adopted methodology for this project is vu process model. Vu process model is a combination of water-fall model and spiral model. This combination has many advantages. This model has high risk analysis so avoidance of risk would be achieved. This model is easy to understand and use. Now first we will discuss the Water-fall model.

Work Plan of Diagnosis of Alzheimer’s Disease Using Machine Learning:

Work plan

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