Early-stage chronic disease prediction is a critical aspect of healthcare that allows for timely interventions and personalized treatment, ultimately improving patient outcomes. In this study, we explore the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, to predict the early stages of chronic diseases such as diabetes, cardiovascular diseases, and respiratory conditions. We compare the performance of LSTM with traditional machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression. The results show that LSTM outperforms the other models in terms of accuracy, precision, recall, F1-score, and AUC, demonstrating its superior ability to capture complex, temporal dependencies in medical data. The study highlights the potential of deep learning for early disease detection and its implications for personalized medicine, telemedicine, and healthcare optimization. However, challenges related to data quality, interpretability, and model generalization across diverse populations remain, and future work should address these issues to enhance the real-world applicability of AI-driven healthcare solutions.

Chronic disease prediction, Early-stage disease detection, Deep learning, Long Short-Term Memory (LSTM)

Bai, C., Zhang, G., & Wu, S. (2019). Machine learning models for demand forecasting in the retail industry: A review. International Journal of Production Economics, 217, 230-243. https://doi.org/10.1016/j.ijpe.2019.03.021

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

Chen, Y., Wang, D., & Zhang, J. (2021). Deep learning for multi-echelon demand forecasting in supply chains. Computers & Industrial Engineering, 149, 107272. https://doi.org/10.1016/j.cie.2021.107272

Chong, A. Y. L., Ch'ng, E., & Li, B. (2017). Predicting demand for e-commerce: A machine learning approach. Computers & Industrial Engineering, 113, 118-126. https://doi.org/10.1016/j.cie.2017.09.017

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451

Han, J. (2020). Logistic regression and its applications. Springer.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Li, C., Zhang, J., & Lee, L. H. (2020). Demand forecasting in multi-echelon supply chains: A deep learning approach. Computers & Industrial Engineering, 149, 106775. https://doi.org/10.1016/j.cie.2020.106775

Suganthi, L., & Sumathi, M. (2020). Hybrid models for chronic disease prediction: A review of approaches and techniques. Journal of Biomedical Informatics, 108, 103503. https://doi.org/10.1016/j.jbi.2020.103503

Shi, Y., Xie, L., & Zhang, L. (2019). Demand forecasting using LSTM network: A case study in retail. Proceedings of the 2019 International Conference on Data Mining and Big Data, 130-135. https://doi.org/10.1109/ICDMW.2019.00036

Xie, Y., Zhang, Y., & Li, L. (2018). A deep learning framework for demand forecasting in supply chain. International Journal of Production Research, 56(19), 6004-6017. https://doi.org/10.1080/00207543.2018.1444279

Zhang, Y., Yu, H., & Zhang, Z. (2018). Hybrid forecasting model based on ARIMA and support vector machine for demand forecasting in supply chain. Applied Soft Computing, 70, 452-462. https://doi.org/10.1016/j.asoc.2018.06.017

Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Nath, F., Chowdhury, M. O. S., & Rhaman, M. M. (2023). Navigating produced water sustainability in the oil and gas sector: A Critical review of reuse challenges, treatment technologies, and prospects ahead. Water, 15(23), 4088.

PHAN, H. T. N., & AKTER, A. (2024). HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES. Universal Publication Index e-Library, 63-76.

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).

Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.

Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.

Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.

Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Nguyen, A. T. P., Jewel, R. M., & Akter, A. (2025). Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 15-26.

Nguyen, A. T. P., Shak, M. S., & Al-Imran, M. (2024). ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES. International Journal of Medical Science and Public Health Research, 5(12), 119-133.

Phan, H. T. N., & Akter, A. (2025). Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 27-37.

Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.

Mazharul Islam Tusher, “Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation”, ijmsphr, vol. 6, no. 05, pp. 63–72, May 2025.

Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry”., Int Bus & Eco Adv Jou, vol. 6, no. 05, pp. 90–101, May 2025, doi: 10.55640/business/volume06issue05-05.

Tamanna Pervin, Sharmin Akter, Sadia Afrin, Md Refat Hossain, MD Sajedul Karim Chy, Sadia Akter, Md Minzamul Hasan, Md Mafuzur Rahman, & Chowdhury Amin Abdullah. (2025). A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy. The American Journal of Management and Economics Innovations, 7(04), 116–123. https://doi.org/10.37547/tajmei/Volume07Issue04-15

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Mazharul Islam Tusher, Han Thi Ngoc Phan, Arjina Akter, Md Rayhan Hassan Mahin, & Estak Ahmed. (2025). A Machine Learning Ensemble Approach for Early Detection of Oral Cancer: Integrating Clinical Data and Imaging Analysis in the Public Health. International Journal of Medical Science and Public Health Research, 6(04), 07–15. https://doi.org/10.37547/ijmsphr/Volume06Issue04-02

Safayet Hossain, Ashadujjaman Sajal, Sakib Salam Jamee, Sanjida Akter Tisha, Md Tarake Siddique, Md Omar Obaid, MD Sajedul Karim Chy, & Md Sayem Ul Haque. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22–33. https://doi.org/10.37547/tajet/Volume07Issue04-04

Ayub, M. I., Bhattacharjee, B., Akter, P., Uddin, M. N., Gharami, A. K., Islam, M. I., ... & Chambugong, L. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141-150.

Siddique, M. T., Uddin, M. J., Chambugong, L., Nijhum, A. M., Uddin, M. N., Shahid, R., ... & Ahmed, M. (2025). AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. International Interdisciplinary Business Economics Advancement Journal, 6(05), 135-147.

Thakur, K., Sayed, M. A., Tisha, S. A., Alam, M. K., Hasan, M. T., Shorna, J. F., ... & Ayon, E. H. (2025). Multimodal Deepfake Detection Using Transformer-Based Large Language Models: A Path Toward Secure Media and Clinical Integrity. The American Journal of Engineering and Technology, 7(05), 169-177.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Tusher, M. I., Hasan, M. M., Akter, S., Haider, M., Chy, M. S. K., Akhi, S. S., ... & Shaima, M. (2025). Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation. International Journal of Medical Science and Public Health Research, 6(05), 63-72.

Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.

Paresh Chandra Nath, Md Sajedul Karim Chy, Md Refat Hossain, Md Rashel Miah, Sakib Salam Jamee, Mohammad Kawsur Sharif, Md Shakhaowat Hossain, & Mousumi Ahmed. (2025). Comparative Performance of Large Language Models for Sentiment Analysis of Consumer Feedback in the Banking Sector: Accuracy, Efficiency, and Practical Deployment. Frontline Marketing, Management and Economics Journal, 5(06), 07–19. https://doi.org/10.37547/marketing-fmmej-05-06-02

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Jamee, S. S., Sajal, A., Obaid, M. O., Uddin, M. N., Haque, M. S. U., Gharami, A. K., ... & FARHAN, M. (2025). Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. International Interdisciplinary Business Economics Advancement Journal, 6(05), 90-101.

Hossain, S., Sajal, A., Jamee, S. S., Tisha, S. A., Siddique, M. T., Obaid, M. O., ... & Haque, M. S. U. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22-33.

Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES