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An application that allows users to predict the risk of a patient having dementia based from their MRI Scan and other medical data using Three Ensemble machine learning methods

Jupyter Notebook 90.36% Procfile 0.02% Python 4.29% JavaScript 0.20% CSS 0.48% HTML 4.66%

ensemble-dementia-predictor's Introduction

Prediction of Dementia using Three Ensemble Machine Learning Methods: Project Overview

An application that allows users to predict the risk of a patient having dementia based from their MRI Scan and other medical data

Three types of Ensemble methods were performed to model the data .i.e Bagging, Stacking and Boosting.
- Bagging using Random Forest
- Stacking using Decision Trees, Naive Bayes and K-Nearest Neighbors
- Boosting using XGBoost
Performed various Data Preprocessing techniques such as missing data imputation and removal of multicolinear features to clean and make the data ready for model building
Tuned hyperparameters of the model to achieve best performance.
Boosting had an accuracy of 86.76%, f1-score of 83.64% and recall of 85.19%
Model was deployed on a web application built using Django available at Dementia Predictor

Model Performamce

Accuracy, F1-Score and Recall were the metrics used to evaluate the performance of the model

Method	Accuracy (%)	F1-Score (%)	Recall (%)
Bagging	85.29	82.14	85.19
Stacking	85.29	80.77	77.78
Boosting	86.76	83.64	85.19

Confusion Matrix

	0	1
0	TN	FP
1	FN	TP

Features

Variable	Data Object	Data type
MR Delay	The number of days between visits by a patient.	Integer
Gender	Gender of a patient (M or F)	Object
Hand	Patient’s dominant hand	Object
Age	Patient's age at the time of data collection	Integer
EDUC	Years of Education	Integer
SES	Socioeconomic status is classified into categories from 1 (highest status) to 5 (lowest status)	Integer
MMSE	Mini-mental State Examination score (range is from 0-worst to 30-best)	Integer
eTIV	Estimated total intracranial volume (cm3)	Integer
nWBV	Normalized whole brain volume	Float

Model Deployment

The final model with the best score was deployed on a web application built with Django with the frontend built with HTML & CSS with Boostrap 4 as the CSS Framework.