A supervised learning approach for recommending medical specialists in the healthcare sector for the Afaan Oromo context DOI Creative Commons
Etana Fikadu Dinsa, Mrinal Kanti Das, Teklu Urgessa

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: April 24, 2025

Abstract In healthcare institutions, an automated system plays a critical role by enhancing patients’ satisfaction with service delivery. This paper focused on the development of model that assists patients in finding appropriate medical specialists Afaan Oromo. To do this, text preprocessing tasks were applied to data remove unnecessary texts, punctuation, and numbers, as they would be suitable for training model. A feature extraction task is obtain standard Oromo health dataset using TF-IDF. We used supervised learning algorithms such logistic regression, random forest, multi-layer perceptron, decision trees, Bi-LSTM, K-NN experimental purposes. Evaluation measures comparing performance seven specialist classes labeled dataset. comparative analysis, result reveals Bi-LSTM performed well, achieving equal value accuracy F1 score, which 0.9708. Based results, user interface was developed proposed method, highest-outperformed detect symptoms predict specialists.

Language: Английский

MLP-SVM: a hybrid approach for improving the performance of the classification model for health-related documents from social media using multi-layer perceptron and support vector machine DOI Creative Commons
Etana Fikadu Dinsa, Teklu Urgessa, Mrinal Kanti Das

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(4)

Published: April 9, 2025

Language: Английский

Citations

0
A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information DOI Creative Commons
Etana Fikadu Dinsa, Mrinal Kanti Das,

Teklu Urgessa Abebe

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

Language: Английский

Citations

3
A supervised learning approach for recommending medical specialists in the healthcare sector for the Afaan Oromo context DOI Creative Commons
Etana Fikadu Dinsa, Mrinal Kanti Das, Teklu Urgessa

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: April 24, 2025

Abstract In healthcare institutions, an automated system plays a critical role by enhancing patients’ satisfaction with service delivery. This paper focused on the development of model that assists patients in finding appropriate medical specialists Afaan Oromo. To do this, text preprocessing tasks were applied to data remove unnecessary texts, punctuation, and numbers, as they would be suitable for training model. A feature extraction task is obtain standard Oromo health dataset using TF-IDF. We used supervised learning algorithms such logistic regression, random forest, multi-layer perceptron, decision trees, Bi-LSTM, K-NN experimental purposes. Evaluation measures comparing performance seven specialist classes labeled dataset. comparative analysis, result reveals Bi-LSTM performed well, achieving equal value accuracy F1 score, which 0.9708. Based results, user interface was developed proposed method, highest-outperformed detect symptoms predict specialists.

Language: Английский

Citations

0