Genetic algorithm-based decision tree optimization for detection of dementia through MRI analysis

• Autorzy: Anuradha Govada , Davu Harini , Karri Muthyalanaidu

Identyfikatory

DOI

10.35784/iapgos.5775

Warianty tytułu

PL

Optymalizacja drzewa decyzyjnego oparta na algorytmie genetycznym do wykrywania demencji poprzez analizę MRI

• Języki publikacji: EN

Abstrakty

EN

Dementia is a devastating neurological disorder that affects millions of people globally, causing progressive decline in cognitive function and daily living activities. Early and precise detection of dementia is critical for optimal dementia therapy and management however, the diagnosis of dementia is often challenging due to the complexity of the disease and the wide range of symptoms that patients may exhibit. Machine learning approaches are becoming progressively more prevalent in the realm of image processing, particularly for disease prediction. These algorithms can learn to recognize distinctive characteristics and patterns that are suggestive of specific diseases by analyzing images from multiple medical imaging modalities. This paper aims to develop and optimize a decision tree algorithm for dementia detection using the OASIS dataset, which comprises a large collection of MRI images and associated clinical data. This approach involves using a genetic algorithm to optimize the decision tree model for maximum accuracy and effectiveness. The ultimate goal of the paper is to develop an effective, non-invasive diagnostic tool for early and accurate detection of dementia. The GA-based decision tree, as proposed, exhibits strong performance compared to alternative models, boasting an impressive accuracy rate of 96.67% according to experimental results.

PL

Demencja jest wyniszczającym zaburzeniem neurologicznym, które dotyka miliony ludzi na całym świecie, powodując postępujący spadek funkcji poznawczych i codziennych czynności życiowych. Wczesne i precyzyjne wykrywanie demencji ma kluczowe znaczenie dla optymalnej terapii i zarządzania demencją, jednak diagnoza demencji jest często trudna ze względu na złożoność choroby i szeroki zakres objawów, które mogą wykazywać pacjenci. Podejścia oparte na uczeniu maszynowym stają się coraz bardziej powszechne w dziedzinie przetwarzania obrazu, szczególnie w zakresie przewidywania chorób. Algorytmy te mogą nauczyć się rozpoznawać charakterystyczne cechy i wzorce, które sugerują określone choroby, analizując obrazy z wielu modalności obrazowania medycznego. Niniejszy artykuł ma na celu opracowanie i optymalizację algorytmu drzewa decyzyjnego do wykrywania demencji przy użyciu zbioru danych OASIS, który obejmuje duży zbiór obrazów MRI i powiązanych danych klinicznych. Podejście to obejmuje wykorzystanie algorytmu genetycznego do optymalizacji modelu drzewa decyzyjnego w celu uzyskania maksymalnej dokładności i skuteczności. Ostatecznym celem artykułu jest opracowanie skutecznego, nieinwazyjnego narzędzia diagnostycznego do wczesnego i dokładnego wykrywania demencji. Zaproponowane drzewo decyzyjne oparte na GA wykazuje wysoką wydajność w porównaniu z alternatywnymi modelami, szczycąc się imponującym współczynnikiem dokładności wynoszącym 96,67% zgodnie z wynikami eksperymentalnymi.

Słowa kluczowe

EN

dementia; genetic algorithm; decision tree

PL

demencja; algorytm genetyczny; drzewo decyzyjne

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 1
• Strony: 83--89
• Opis fizyczny: Bibliogr. 40 poz., tab., wykr.

Twórcy

autor

Anuradha Govada

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Kanuru, Vijayawada, India

• https://orcid.org/0000-0002-0999-0376

autor

Davu Harini

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Kanuru, Vijayawada, India

• https://orcid.org/0009-0008-6187-1797

autor

Karri Muthyalanaidu

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Kanuru, Vijayawada, India

• https://orcid.org/0009-0006-5850-3761

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