Alzheimer's disease detection

Hi all! This is a alzheimer disease detection application, and this comprises of detailed Data Analysis and a state of the art deep learning model. It predicts if an old aged man has alzheimer's or not, using some of the patient's data mentioned below (with their meaning):

• Age: The age of the patients ranges from 60 to 90 years.
• Gender: Gender of the patients, where 0 represents Male and 1 represents Female.
• Ethnicity: The ethnicity of the patients, coded as follows:
  • 0: Caucasian
  • 1: African American
  • 2: Asian
  • 3: Other
• EducationLevel: The education level of the patients, coded as follows:
  • 0: None
  • 1: High School
  • 2: Bachelor's
  • 3: Higher
• BMI: Body Mass Index of the patients, ranging from 15 to 40.
• Smoking: Smoking status, where 0 indicates No and 1 indicates Yes.
• AlcoholConsumption: Weekly alcohol consumption in units, ranging from 0 to 20.
• PhysicalActivity: Weekly physical activity in hours, ranging from 0 to 10.
• DietQuality: Diet quality score, ranging from 0 to 10.
• SleepQuality: Sleep quality score, ranging from 4 to 10.
• FamilyHistoryAlzheimers: Family history of Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.
• CardiovascularDisease: Presence of cardiovascular disease, where 0 indicates No and 1 indicates Yes.
• Diabetes: Presence of diabetes, where 0 indicates No and 1 indicates Yes.
• Depression: Presence of depression, where 0 indicates No and 1 indicates Yes.
• HeadInjury: History of head injury, where 0 indicates No and 1 indicates Yes.
• Hypertension: Presence of hypertension, where 0 indicates No and 1 indicates Yes.
• SystolicBP: Systolic blood pressure, ranging from 90 to 180 mmHg.
• DiastolicBP: Diastolic blood pressure, ranging from 60 to 120 mmHg.
• CholesterolTotal: Total cholesterol levels, ranging from 150 to 300 mg/dL.
• CholesterolLDL: Low-density lipoprotein cholesterol levels, ranging from 50 to 200 mg/dL.
• CholesterolHDL: High-density lipoprotein cholesterol levels, ranging from 20 to 100 mg/dL.
• CholesterolTriglycerides: Triglycerides levels, ranging from 50 to 400 mg/dL.
• MMSE: Mini-Mental State Examination score, ranging from 0 to 30. Lower scores indicate cognitive impairment.
• FunctionalAssessment: Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment.
• MemoryComplaints: Presence of memory complaints, where 0 indicates No and 1 indicates Yes.
• BehavioralProblems: Presence of behavioral problems, where 0 indicates No and 1 indicates Yes.
• ADL: Activities of Daily Living score, ranging from 0 to 10. Lower scores indicate greater impairment.
• Confusion: Presence of confusion, where 0 indicates No and 1 indicates Yes.
• Disorientation: Presence of disorientation, where 0 indicates No and 1 indicates Yes.
• PersonalityChanges: Presence of personality changes, where 0 indicates No and 1 indicates Yes.
• DifficultyCompletingTasks: Presence of difficulty completing tasks, where 0 indicates No and 1 indicates Yes.
• Forgetfulness: Presence of forgetfulness, where 0 indicates No and 1 indicates Yes.
• Diagnosis: Diagnosis status for Alzheimer's Disease, where 0 indicates There is no risk and 1 indicates There is a risk.

Using this data, the model predicts diagnosis and gives the information whether the patient is at the risk of having an alzheimer's disease or not.

Data Analysis

In Data Analysis, it was found that there are no null values present in the dataset. 2 of the columns(PatientID,DoctorInCharge) have been deleted from the dataset because it does not serve any purpose for predicting the diagnosis. Using box plots, it was understood that there are no outliers in the dataset, and there were no object type columns to convert.

By understanding the data, it was found that patients with no head injury and with cardiovascular disease were at a higher risk of getting Alzheimer's. All this code is in the EDA part of the data_and_eda folder.

Model Training

I found out that a deep learning sequential model with 6 layers (5 input layers, and 1 output layer),using adam optimizer,accuracy as the metrics, and activation function as relu, was the best model for this particular use-case. A significant amount of trials were done before reaching to this stage, some of them being increasing the number of input layers to 10 or more, and changing optimizers and activation functions, but then the best accuracy was with this particular deep learning model.

Using the code

First, prerequisites to download this particular application:

• Git installed on your machine.

• Python installed on your machine.

  git clone https://github.com/syphxncodes/Alzheimer-Disease-Detection.git

Change the directory of your command prompt to the cloned repo

  cd Alzheimer-Disease-Detection

Create a virtual enviroment for this to run.

python -m venv venv

• On Windows:

 venv\Scripts\activate

• On macOS/Linux:

 source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Run

python app.py

Then, go to any web browser and paste: http://127.0.0.1:5000/predictdata

There you go! Just type in your values, and hopefully ur patient is Alzheimer Safe! Thank You for visiting!!