GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms

Abstract

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). Graphical abstract

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

“…By combining an interpretable graph neural network with the dataset collected from ADNI, Mansu et al [122] bridge the gap between efficiently integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to develop precise and understandable systems. Fernando et al [155] presents a Python-based computational tool to deal with the data obtained during clinical diagnosis. The tool integrates data processing, designing predictive models and features of XAI for explainability.…”

“…The suggested strategy addresses the problem of ongoing remote monitoring of elderly individuals to aid in the early identification of cognitive decline and to better assist clinicians in reaching a diagnosis. In another study, Fernando et al [155] proposed a diagnostic tool that uses a decision tree that provides a simple and unambiguous set of decision rules to provide capabilities to clinicians to give insights into the pathophysiology of AD and behavioural Fronto Temporal Dementia (bvFTD). This paper is beneficial for early detection and diagnosis in the medical field because it outlines all the processes needed to evaluate the datasets, including data preparation, selection of features using an evolutionary approach, and in the creation of a model for the disease discussed in the paper.…”

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

“…A third issue is that existing DL-assisted diagnostic studies are still reliant on expert-level preprocessing based on hypotheses. Some ML tools help to distinguish the AD and FTD symptoms with genetic algorithms [ 70 ]. It has been demonstrated that a data-centric perspective helps to understand AD and FTD disorders by allowing the results to be interpreted.…”

Bayesian Network Modelling for the Clinical Diagnosis of Alzheimer’s Disease

“…There have been several Bayesian models proposed that were not causal BN models 22 including naïve Bayes models 23 . Recently García-Gutierrez et al proposed a two-layered model for the diagnosis of AD and Frontotemporal Dementia (FTD) 24 . The first layer executes binary classification using SVMs.…”