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https://github.com/shashankks0709/dementia-using-machine-learning

Detection of Dementia using machine learning
https://github.com/shashankks0709/dementia-using-machine-learning

dimentia jupyter-notebook machine-learning machine-learning-algorithms pycharm-ide python

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Detection of Dementia using machine learning

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# Dementia-using-machine-learning

Detection of Dementia using machine learning and python 



✅Abstract :

Dementia is a neurodegenerative disorder, which is one of the most common among older

people, it constitutes one of the diseases with great social impact in Europe and America. The

progress of medical diagnosis using magnetic resonance imaging (MRI) is widely used for

the treatment of neurological diseases; it allows obtaining, increasingly, more functional and

anatomical information from the brain of the patients, with a great precision in time and

space. This model proposes a methodology for the diagnosis of dementia based on

Alzheimer’s disease combining imaging processing and artificial intelligence techniques by

creating an artificial neural network (ANN) of classification based on architecture Multilayer

Perceptron. In order to construct a complete dataset for training and testing of the network,

initial inputs-target variables were obtained from the database OASIS (Open Access Series of

imaging studies) with a total of instances equal to 235. The variables were classified into 3

groups: demographic, clinical and morphometric data. Task of training and testing were

applied on initial data which would result in 48% confusion error. For minimizing this

percentages of error, image processing and Voxel based morphometry (VBM) techniques

were implemented to obtain new morphometric variables of three areas of the brain: white

matter (WM), gray matter (GM) and fluid (CSF) cerebro-espinal. In this way, by reducing the

percentage of confusion to 17%, the results obtained with the ANN, demonstrates that the

demographic and clinical information from patients, combined with morphometric

information of areas of the brain, are input variables useful to train an ANN of diagnosis of

dementia with 83 % of reliability, and in this way, help to the early diagnosis of Dementia.



✅Introduction:

Dementia is a global health issue. Dementia is a state describing some group of symptoms which

can occur when few groups of cells of brain stop working in an appropriate manner. Dementia

normally occurs inside the particular regions of the brain that influences the way one can think,

recall and convey. Dementia usually occurs due to Alzheimer’s disease. Dementia occurring

before 65 years of age is said to be as early-onset dementia whereas after 65 years is said as lateonset dementia. 

The World Alzheimer Report of the year 2015 shows the global number of

people suffering from dementia are 46.8 million and an increase of 74.7 million is estimated by

2030 and 131.5 million by 2050. The types of Dementia are Alzheimer’s Disease,

Frontotemporal Dementia, Dementia with Lewy Bodies and Vascular Dementia. Alzheimer’s

Disease is responsible for almost 75% of the overall cases. A state when both Alzheimer’s and

Vascular Dementia occur simultaneously is called as Mixed Dementia. Although there are a

number of medical areas to which Machine Learning ( ML ) systems have been applied,

dementia has not been one of them. Yet dementia is a complex problem with correct diagnosis

requiring historical data, physical exam, cognitive testing, laboratory studies and imaging.

Although diagnostic accuracy using clinical NINCDS-ADRDA criteria for probable AD 1 is

about 88% compared to post-mortem diagnosis, most demented patients are seen by community

physicians who often do not detect dementia or misdiagnosis it. This problem is compounded by

the average delay of 4 years between symptom onset and physician contact, which usually relates

to the patient's social embarrassment about having a memory problem. At this point of the

disease, physicians are less able to slow the progression and minimize debilitating behavioral

changes of the dementia. A simple, method for detecting dementia early in the disease's course

would help get patients to seek early evaluation and treatment.

 

 ✅Tools Used :

 Jupyter Notebook,

 Pycharm

 

 ✅Purpose of the Project:

Early detection and correct diagnosis can lead to disease-retarding therapies which can slow

disease progression and reduce patient and caregiver stress and morbidities. After excluding

delirium and depression, two forms, the Mini mental state examination( MIMSE ) can be used to

determine if a person has dementia, which is defined as multiple cognitive impairments with loss

of functional skills related to those cognitive impairments without an altered level of

consciousness. The ANN and J48 methods can simplify the task of interpreting test results by

constructing a set of criteria to classify the patient. In our project we report the use of the

MIMSE , eTIV tests in conjunction with ML methods. The goal was to determine whether ML

methods improve the accuracy of diagnosis of these dementia screening tests for classifying a

subject as either demented or not demented. By feeding data with machine learning techniques,

we can significantly cut down on the amount of people suffering from dementia that are

undiagnosed So having model that can take a vast amount of data, and automatically identify

patients with possible dementia, to facilitate targeted screening, could potentially be very useful

and help improve diagnosis rates.



✅Scope:

The model proposes a methodology for the diagnosis of dementia based on Alzheimer’s disease

combining imaging processing, machine learning(j48),artificial intelligence techniques by

creating an artificial neural network (ANN) of classification based on architecture Multilayer

Perceptron. In order to construct a complete dataset for training and testing of the network, initial

inputs-target variables were obtained from the database OASIS (Open Access Series of imaging

studies) with a total of instances equal to 235. The variables were classified into 3 groups:

demographic, clinical and morphometric data. Task of training and testing were applied on initial

data which would result in 48% confusion error. For minimizing this percentages of error, image

processing and Voxel based morphometry (VBM) techniques are being implemented to obtain

new morphometric variables of three areas of the brain: white matter (WM), gray matter (GM)

and fluid (CSF) cerebroespinal. In this way, by reducing the percentage of confusion to 17%, the

results obtained with the ANN, would be able to demonstrate that the demographic and clinical

information from patients, combined with morphometric information of areas of the brain, are

input variables useful to train an ANN of diagnosis of dementia with 83 % of reliability, and in

this way, help to the early diagnosis of Dementia.



✅Existing System:

With the expected growth in dementia prevalence, the number of specialist memory clinics may

be insufficient to meet the expected demand for diagnosis. Furthermore, although current ’gold

standards’ in dementia diagnosis may be effective, they involve the use of expensive

neuroimaging (for example, positron emission tomography scans) and time-consuming

neuropsychological assessments which is not ideal for routine screening of dementia.

In the proposed model, the dataset is collected from the OASIS-Brains.org . The Open Access

Series of Imaging Studies (OASIS) which allows free availability of the brain MRI data sets. The

OASIS dataset contains Longitudinal MRI data of non-demented and demented older adults. We

have applied the following classification techniques: J48 and Multilayer Perceptron to the

dataset. And further, Attribute Selection technique is also applied using CFSSubsetEval. The

attributes included in the OASIS dataset are age, sex, education, socioeconomic status, minimental state examination, 

clinical dementia rating , atlas scaling factor, estimated total

intracranial volume, and normalized whole-brain volume.



✅Problem Statement:

The aim of this study is to develop a machine learning-based model that could be used in general

practice to detect dementia from collected data and brain MRI images. The model would be a

useful tool for identifying people who may be living with dementia but have not been formally

diagnosed or have a proclivity towards the disease.



✅Summary:

So having model that can take a vast amount of data, and automatically identify patients with

possible dementia, to facilitate targeted screening, could potentially be very useful and help

improve diagnosis rates. Hence with machine learning techniques like Multilayer perceptron and

j48 along with Mini mental state examination helps in achieving the task. Thus, we can

significantly cut down on the amount of people suffering from dementia that are undiagnosed.