Applying Prompt Engineering of Generative Artificial Intelligence to the Diagnosis of Diabetes
This paper presents a system that relies on artificial intelligence models with enough sophistication built by rigorously adhering to the recommendations of generative artificial intelligence tools such as large language models when prompted to design a system for the automated diagnosis of diabetes. Actual source code for the construction of the artificial intelligence models is generated as part of the suggestions recommended by the generative artificial intelligence tools or large language models. By faithfully incorporating the source code into a module for the automated diagnosis of diabetes based on clinical measurements, adequately sophisticated artificial intelligence models are constructed, trained, tested and validated on publicly accessible diabetes datasets and then deployed in an automated diabetes diagnosis module. Results indicate that the resulting artificial intelligence models exhibit reasonable performance that compares favorably (in view of the fact that the resulting artificial intelligence models are not optimized) with the performance of systems designed from the ground up by artificial intelligence experts.
World Health Organization (WHO) Diabetes Statistics: https://www.who.int/news-room/fact-sheets/detail/diabetes. (Retrieved 2025).
Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., Yoneda, T. Artificial Intelligence in Current Diabetes Management and Prediction, Curr Diab Rep. 21(12):61 (2021).
Kumar, Y., Koul, A., Singla, R., Ijaz, M. F. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda, Journal of Ambient Intelligence and Humanized Computing 14:8459–8486 (2023).
Ansari, S., Shafi, I., Ansari, A., Ahmad, J., Shah, S. I. Diagnosis of liver disease induced by hepatitis virus using artificial neural network, IEEE Int Multitopic. https://doi.org/10.1109/INMIC.2011.6151515 (2011).
Battineni, G., Sagaro, G. G., Chinatalapudi, N., Amenta, F. Applications of machine learning predictive models in the chronic disease diagnosis, J Personal Med. https://doi.org/10.3390/jpm10020021 (2020).
Abdar, M., Yen, N., Hung, J. Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision tree, J Med Biol Eng 38:953–965 (2018).
Chaikijurajai, T., Laffin, L., Tang, W. Artificial intelligence and hypertension: recent advances and future outlook, Am J Hypertens 33:967–974 (2020).
Fujita, S., Hagiwara, A., Otsuka, Y., Hori, M., Takei, N., Hwang, K. P., Irie, R., Andica, C., Kamagata, K., Akashi, T., Kumamaru, K. K., Suzuki, M., Wada, A., Abe, O., Aoki, S. Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans, Invest Radiol 55:249–256 (2020).
Juarez-Chambi, R. M., Kut, C., Rico-Jimenez, J. J., Chaichana, L. K., Xi, J., Campos-Delgado, D. U., Rodriguez, F. J., Quinones-Hinojosa, A., Li, X., Jo, J. A. AI-Assisted In Situ Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography, Clin Cancer Res 25(21):6329–6338 (2019).
Nashif, S., Raihan, R., Islam, R., Imam, M. H. Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System, World Journal of Engineering and Technology Vol 6, No. 4 (2018).
Chen, P. H. C., Gadepalli, K., MacDonald, R., Liu, Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., Mermel, C. H., Stumpe, M. C. An augmented reality microscope with real time artificial intelligence integration for cancer diagnosis, Nat Med 25:1453–1457 (2019).
Gouda, W., Yasin, R. COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity, Egypt J Radiol Nucl Med 51(1):196 (2020).
Han, Y., Han, Z., Wu, J., Yu, Y., Gao, S., Hua, D., Yang, A. Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology, IEEE Access 8:44924–44935 (2020).
Chui, C. S., Lee, N. P., Adeoye, J., Thomson, P., Choi, S. W. Machine learning and treatment outcome prediction for oral cancer, J Oral Pathol Med 49(10):977–985 (2020).
Koshimizu, H., Kojima, R., Okuno, Y. Future possibilities for artificial intelligence in the practical management of hypertension, Hypertens Res 43:1327–1337 (2020).
Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., Grabsch, H. I., Yoshikawa, T., Brenner, H., Chang-Claude, J., Hoffmeister, M., Trautwein, C., Luedde, T. Deep learning microsatellite instability directly from histology in gastrointestinal cancer, Nat Med 25:1054–1056 (2019).
Kwon, J. M., Jeon, K. H., Kim, H. M., Kim, M. J., Lim, S. M., Kim, K. H., Song, P. S., Park, J., Choi, R. K., Oh, B. H. Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography, EP Europace 22(3):412–419 (2020).
Khan, M. A. An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier, IEEE Access 8:34717–34727 (2020).
Oikonomou, E. K., Williams, M. C., Kotanidis, C. P., Desai, M. Y., Marwan, M., Antonopoulos, A. S., Thomas, K. E., Thomas, S., Akoumianakis, I., Fan, L. M., Kesavan, S., Herdman, L., Alashi, A., Centeno, E. H., Lyasheva, M., Griffin, B. P., Flamm, S. D., Shirodaria, C. Sabharwal, N., Kelion, A., Dweck, M. R., Van Beek, E. J. R., Deanfield, J., Hopewell, J. C., Neubauer, S., Channon, K. M., Achenbach, S., Newby, D. E., Antoniades, C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography, Eur Heart J 40(43):3529–3543 (2019).
Sabottke, C. F., Spieler, B. M. The Effect of Image Resolution on Deep Learning in Radiography, Radiology: Artificial Intelligence Vol. 2. No. 1, 2:e190015 (2020).
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D. Language Models are Few-Shot Learners, Advances in Neural Information Processing Systems 33, (2020).
Gurnee, W., Tegmark, M. Language Models Represent Space and Time, arXiv, DOI: https://doi.org/10.48550/arXiv.2310.02207 (2023).
Ekpar, F. E. Leveraging Generative Artificial Intelligence Recommendations for Image-based Chronic Kidney Disease Diagnosis, International Journal of Advanced Research in Computer and Communication Engineering, 14(1). (2025).
Ekpar, F. E. Generative Artificial Intelligence Recommendations for Clinical Measurement-Based Diagnosis of Chronic Kidney Disease, International Journal of Engineering And Computer Science, 14(1). (2025).
Ekpar, F. E. A Comprehensive Artificial Intelligence-Driven Healthcare System, European Journal of Electrical Engineering and Computer Science, 8(3), Article 617. (2024).
Ekpar, F. E. Image-based Chronic Disease Diagnosis Using 2D Convolutional Neural Networks in the Context of a Comprehensive Artificial Intelligence-Driven Healthcare System, WSEAS Molecular Sciences and Applications, 4(13). (2024).
Ekpar, F. E. Diagnosis of Diabetes Within a Comprehensive Artificial Intelligence-Driven Healthcare System, International Journal of Advanced Research in Computer and Communication Engineering, 13(8). (2024).
Ekpar, F. E. Diagnosis of Chronic Kidney Disease Within a Comprehensive Artificial Intelligence-Driven Healthcare System, International Journal of Advanced Research in Computer and Communication Engineering, 13(9). (2024).
Padfield, N., Zabalza, J., Zhao, H, Masero, V., Ren, J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges, Sensors 19, 1423. (2019).
Kevric, J., Subasi, A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system, Biomedical Signal Processing and Control 31, 398-406. (2017).
Cho, H., Ahn, M., Ahn, S., Kwon, M., Jun, S. C. EEG datasets for motor imagery brain–computer interface, GigaScience Vol. 6, Iss. 7. (2017).
Arpaia, P., Esposito, A., Natalizio, A., Parvis, M. How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art, J. Neural Eng. 19 031002. (2022).
Kaya, M., Binli, M, K., Ozbay, E., Yanar, H., Mishchenko, Y. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces, Scientific Data 5, Article number: 180211 (2018).
Tibrewal, N., Leeuwis, N., Alimardani, M. Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users, PLoS One 17(7): e0268880. (2022).
Sreeja, S. R., Rabha, J., Nagarjuna, K. Y., Samanta, D., Mitra, P., Sarma, M. Motor Imagery EEG Signal Processing and Classification Using Machine Learning Approach, IEEE International Conference on New Trends in Computing Sciences (ICTCS) (2017).
Das, K., Pachori, R. B. Electroencephalogram-Based Motor Imagery Brain–Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering, IEEE Transactions on Cognitive and Developmental Systems, Vol. 15, Iss. 3. (2022).
Velasco, I., Sipols, A., Simon De Blas, C., Pastor, L., Bayona, S. Motor imagery EEG signal classification with a multivariate time series approach, Biomedical Engineering Online 22, Article Number: 29. (2023).
Yang, A., Lam, H. K., Ling, S. H. Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network, Neural Computing and Applications, Vol.35, 12001-12027. (2023).
Venkatachalam, K., Devipriya, A., Maniraj, J., Sivaram, M., Ambikapathy, A., Amiri, I. S. A novel method of motor imagery classification using eeg signal, Artificial Intelligence in Medicine, Vol.103, 101787. (2020).
Subasi, A., Gursov, M. I. EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications, Vol.37, Iss. 12, 8659-8666. (2010).
Razzak, I., Hameed, I. A., Xu, G. Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals, IEEE Journal of Translational Engineering in Health and Medicine, 7:2000508. (2019).
Pahuja, S. K., Veer, K. Recent Approaches on Classification and Feature Extraction of EEG Signal: A Review, Robotica, Vol. 40, Iss. 1, 77-101. (2022).
Lekshmi, S. S., Selvam, V., Rajasekaran, M. P. EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network, IEEE International Conference on Communication and Signal Processing, (2014).
Lugger, K., Flotzinger, D., Schlögl, A., Pregenzer, M., Pfurtscheller, G. Feature extraction for on-line EEG classification using principal components and linear discriminants, Med Biol Eng Comp, 36(3):309-14. (1998).
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., Lance, B. J. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces, Journal of Neural Engineering, 15: 056013. (2018).
Al-Saegh, A., Dawwd, S. A., Abdul-Jabbar, J. M. Deep learning for motor imagery EEG-based classification: A review, Biomedical Signal Processing and Control Vol. 63, 102172. (2021).
Tabar, Y. R., Halici, U. A novel deep learning approach for classification of EEG motor imagery signals, Journal of Neural Engineering, 14: 016003. (2016).
Ekpar, F. E. A Novel Three-dimensional Multilayer Electroencephalography Paradigm, Fortune Journal of Health Sciences, 7(3). (2024).
Ekpar, F. E. System for Nature-Inspired Signal Processing: Principles and Practice, European Journal of Electrical Engineering and Computer Science, 3(6), pp. 1-10, (2019).
Ekpar, F. E. Nature-inspired Signal Processing, United States Patent and Trademark Office, US Patent Application Number: 13/674,035 (Filed: November 11, 2012, Priority Date: December 24, 2011), Document ID: US 20140135642 A1: Published: (2014).
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X. TensorFlow: A System for Large Scale Machine Learning, Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). (2016).
Pang, B., Nijkamp, E., Wu, Y. N. Deep Learning With TensorFlow: A Review, Journal of Educational and Behavioral Statistics. Vol. 45, Iss. 2. (2019).
Kingma, D. P., Ba, J. L. Adam: A Method for Stochastic Optimization, International Conference on Learning Representations (ICLR) (2015).
Zhang, Z. Improved Adam Optimizer for Deep Neural Networks, IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (2018).
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