Identification of medically and forensically relevant flies using a decision treelearning method
https://doi.org/10.47665/tb.40.1.019
- Author:
Tanajitaree, C.
1
;
Sanit, S.
2
;
Sukontason, K.L.
2
;
Sukontason, K.
2
;
Somboon, P.
2
;
Anakkamatee, W.
3
;
Amendt, J.
4
;
Limsopatham, K.
2
Author Information
1. Graduate Masters Degree Program in Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
2. Center of Insect Vector Study, Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
3. Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
4. Institute of Legal Medicine, Goethe University, Frankfurt 60596, Germany
- Publication Type:Journal Article
- Keywords:
Decision tree;
wing morphometric;
identification model;
forensic entomology.
- From:Tropical Biomedicine
2023;40(No.1):80-87
- CountryMalaysia
- Language:English
-
Abstract:
Blow flies, flesh flies, and house flies can provide excellent evidence for forensic entomologists and are
also essential to the fields of public health, medicine, and animal health. In all questions, the correct
identification of fly species is an important initial step. The usual methods based on morphology or
even molecular approaches can reach their limits here, especially when dealing with larger numbers
of specimens. Since machine learning already plays a major role in many areas of daily life, such as
education, business, industry, science, and medicine, applications for the classification of insects
have been reported. Here, we applied the decision tree method with wing morphometric data to
construct a model for discriminating flies of three families [Calliphoridae, Sarcophagidae, Muscidae]
and seven species [Chrysomya megacephala (Fabricius), Chrysomya rufifacies (Macquart), Chrysomya
(Ceylonomyia) nigripes Aubertin, Lucilia cuprina (Wiedemann), Hemipyrellia ligurriens (Wiedemann),
Musca domestica Linneaus, and Parasarcophaga (Liosarcophaga) dux Thomson]. One hundred percent
overall accuracy was obtained at a family level, followed by 83.33% at a species level. The results of
this study suggest that non-experts might utilize this identification tool. However, more species and
also samples per specimens should be studied to create a model that can be applied to the different
fly species in Thailand.
- Full text:8.2023my1392.pdf
