Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks

Soylu Üstkoyuncu P., Üstkoyuncu N.

TURKISH JOURNAL OF MEDICAL SCIENCES, cilt.54, sa.4, ss.710-717, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 54 Sayı: 4
  • Basım Tarihi: 2024
  • Doi Numarası: 10.55730/1300-0144.5840
  • Dergi Adı: TURKISH JOURNAL OF MEDICAL SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, MEDLINE, Veterinary Science Database, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.710-717
  • Erciyes Üniversitesi Adresli: Evet

Özet

Background/aim: Tandem mass spectrometry is helpful in diagnosing amino acid metabolism disorders, organic acidemias, and fatty acid oxidation disorders and can provide rapid and accurate diagnosis for inborn errors of metabolism. The aim of this study was to predict inborn errors of metabolism in children with the help of artificial neural networks using tandem mass spectrometry data.

Materials and methods: Forty-seven and 13 parameters of tandem mass spectrometry datasets obtained from 2938 different patients were respectively taken into account to train and test the artificial neural networks. Different artificial neural network models were established to obtain better prediction performances. The obtained results were compared with each other for fair comparisons.

Results: The best results were obtained by using the rectified linear unit activation function. One, two, and three hidden layers were considered for artificial neural network models established with both 47 and 13 parameters. The sensitivity of model B2 for definitive inherited metabolic disorders was found to be 80%. The accuracy rates of model A3 and model B2 are 99.3% and 99.2%, respectively. The area under the curve value of model A3 was 0.87, while that of model B2 was 0.90.

Conclusion: The results showed that the proposed artificial neural networks are capable of predicting inborn errors of metabolism very accurately. Therefore, developing new technologies to identify and predict inborn errors of metabolism will be very useful.