Using Deep Learning to Predict Disease Progression in Chronic Illnesses

Want all the details? Grab your free PDF here!

DL-Based Prediction Model

Diabetes remains one of the most challenging chronic conditions to manage, largely because, once diagnosed, it can’t be reversed. The goal, then, is early identification and careful tracking to prevent serious complications. Yet most traditional prediction methods rely on just a few indicators, such as glucose levels, HbA1C, BMI, which can only tell part of the story.

To improve accuracy, we need models that can handle more complexity. DL offers exactly that. By processing vast datasets through multilayer neural networks, DL models can account for a broader range of clinical, demographic, and lifestyle variables. They also support the development of recommendation systems that assist healthcare providers in identifying effective treatments, medications, and care pathways.¹

One such system, described in Results in Engineering, is the Real-time Multi-level Chronic Disease Prediction and Recommendation Model (RMCDPRM). It was designed to better capture disease progression by analyzing medical data at multiple levels, making the predictions both more accurate and more contextually aware.

The model begins with a large, multi-source dataset. This data is first normalized to ensure consistency, then grouped using a method called feature depth clustering. This allows the system to distinguish between different stages of disease. The most relevant features are then extracted and fed into a neural network for training.¹

During testing, the model calculates a class-level disease support value (CLDSV) for each feature. These values are passed through to the output layer, where they’re used to compute a disease weight (DW) for each potential condition. The class with the highest DW becomes the predicted outcome.

But the model doesn’t stop at diagnosis. It also assesses what kind of care might be most effective.

Using metrics such as patient curing weight  (PCW) and disease handling support (DHS), it generates medical support (MS) scores for hospitals and clinicians, producing personalized care recommendations based on patient needs.

To evaluate performance, the model was tested on a fabricated dataset of one million records, spanning 30 features including biometric, lifestyle, and demographic data. The results were promising: up to 99 % prediction accuracy, with reduced time complexity. While further validation with real-world data is still needed, the model shows clear potential for clinical use, especially if future versions incorporate a wider range of health indicators.¹

DL-Based Predictive Model for COPD

While DL models for conditions like diabetes have shown promising levels of accuracy and clinical potential, the picture is more complex for other chronic diseases. COPD, for example, presents a different kind of challenge, both in terms of how it progresses as well as how it’s measured.

As the third leading cause of death globally, COPD places a growing burden on health systems. Its course can vary widely from patient to patient, making early identification of high-risk individuals especially important. If clinicians can spot those likely to experience faster decline, they have a better chance of providing timely, targeted interventions and avoiding costly hospitalizations.

Current assessments often rely on a mix of clinical tools. Lung function tests, patient questionnaires, measures of exercise capacity, and exacerbation history. Composite models, such as the BODE index, which combines BMI, airflow obstruction, dyspnea, and exercise ability, offer greater predictive strength than any single measure.²

To evaluate whether DL might further improve prediction in this space, a study published in The Lancet Digital Health conducted the first systematic review and meta-analysis focused on non-regression-based machine learning and DL models for COPD prognosis. The review examined how well these models predicted outcomes like mortality, exacerbations, and functional decline.

A major strength of the review was its methodology: a comprehensive literature search following PRISMA guidelines, independent screening, and rigorous quality assessments using both TRIPOD and PROBAST frameworks.²

In total, 18 studies were included. Yet despite the promise of DL, the findings were cautious.

There was limited evidence that DL or ML models outperformed conventional regression methods, particularly when models were tested on external datasets. Many struggled with generalizability.

Common issues included missing data, low event-to-variable ratios, and a lack of reported uncertainty metrics. Significant variability across studies also made it difficult to draw strong comparative conclusions.

Still, the review pointed to important directions for future work, especially the development of models that can integrate multimodal data and perform advanced 3D analysis. The researchers also emphasized the need for consistent reporting standards. Adhering to tools like PROBAST will be critical to making DL models not just more powerful, but also more reliable and clinically usable.²

DL-Based Early Prediction Framework

The value of early detection can’t be overstated, especially for conditions like diabetes and hypertension, which often progress quietly until complications arise. Catching these diseases in their earliest stages can help prevent serious outcomes like stroke, kidney failure, or heart attack. But doing so requires more than the occasional check-up. It requires ongoing, real-time insight into the body’s changing signals.

This is where deep learning, paired with biometric time-series data, offers significant promise. As more patient data becomes available through hospital records and wearable devices, models that can interpret this continuous stream of information are becoming increasingly relevant to preventive care.

In a study published in the International Journal of Multidisciplinary and Current Research, researchers proposed a DL framework designed specifically for early-stage prediction of chronic diseases using time-series biometric data.³ The model was built on long short-term memory (LSTM) networks, a type of neural network well-suited for learning patterns in sequential data. This made it ideal for tracking health indicators like blood pressure and glucose levels over time.

The model was trained using the Kaggle Chronic Disease Progression Tracker Dataset, which includes real-world patient data. Unlike traditional approaches that rely on pre-selected features, this framework learned directly from raw time-series inputs. This not only reduced the need for manual feature engineering but also helped the model detect subtle trends that might otherwise go unnoticed.

Advanced preprocessing techniques, including k-nearest neighbors (KNN) imputation and Min-Max scaling, were used to clean and normalize the dataset before training. Performance was measured using accuracy and F1-score as key metrics. The results were impressive: 99.2 % accuracy, 99 % precision, and 99.7 % recall.

In practical terms, this kind of model could support clinicians by flagging early warning signs long before a diagnosis would typically occur. It also offers a scalable foundation for large-scale screening systems, especially when integrated with wearable technology or electronic health record platforms.³

This focus on real-time prediction highlights a broader shift in how we approach chronic disease management: not only looking at what’s already happened, but anticipating what might come next. With DL models showing strong results in time-series applications, the next question is how they compare to more traditional machine learning approaches - and whether that added complexity consistently delivers better outcomes. 

DL Vs. Traditional Machine Learning

As DL models continue to show promise, particularly in capturing complex, time-based health patterns, it’s worth asking how they stack up against more established machine learning (ML) approaches. Is the added complexity of deep neural networks justified by better performance?

A study published in the Frontline Medical Sciences and Pharmaceutical Journal explored this question by directly comparing LSTM-based DL models with traditional ML algorithms.⁴ The researchers focused on early-stage prediction for a range of chronic diseases, including diabetes, cardiovascular disease, and respiratory conditions, all of which involve gradual, often subtle changes over time.

LSTM networks were chosen for their ability to model long-term dependencies in sequential data, making them particularly well-suited to chronic disease monitoring. Their performance was evaluated alongside more conventional models like random forest, gradient boosting machines (GBM), and logistic regression.

The results were, in fact, very clear. The LSTM model outperformed its counterparts across every major metric. It achieved 88.7 % accuracy, 85.5 % precision, 86.3 % recall, 85.9 % F1-score, and an AUC of 0.92. These figures suggest that DL models, particularly those designed to handle temporal dynamics, may offer a more robust way to detect early-stage disease signals in complex datasets.⁴

Perhaps most importantly, the model's ability to learn from historical trends allowed it to flag potential issues before they would typically appear in a clinical setting. This not only supports earlier intervention but also reinforces the value of DL tools in managing long-term health risks where timing makes all the difference.

Conclusion

As deep learning continues to find its footing in clinical research, the question is no longer whether these models can make accurate predictions, but how we integrate their insights meaningfully into patient care.

That means more than benchmarking performance. It means understanding where these models fit in the rhythms of real-world practice: in the unpredictability of symptoms, in the gaps of missing data, in the space between clinical certainty and human complexity.

Perhaps the most useful models won’t be the ones that dazzle with accuracy, but the ones that support decisions, spotting what we’ve missed, flagging what’s easy to overlook, helping clinicians ask better questions. Not replacing judgment, but sharpening it.

Which raises the next challenge: how do we build systems that are not only intelligent, but trustworthy? And how do we ensure that their value doesn’t depend on perfect data or perfect conditions, but works in the messiness of real lives?

References and Further Reading

1. Kumar, M. M., Siva, R., & Baskar, M. (2025). Real-time Multi Level Chronic Disease Prediction and Recommendation Model Using Deep Learning. Results in Engineering, 107478. DOI: 10.1016/j.rineng.2025.107478, https://www.sciencedirect.com/science/article/pii/S2590123025035339
2. Smith, L. A. et al. (2023). Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. The Lancet Digital Health, 5(12), e872-e881. DOI: 10.1016/S2589-7500(23)00177-2, https://www.sciencedirect.com/science/article/pii/S2589750023001772
3. Rathna, S. (2023). Deep Learning Framework for Early Prediction of Chronic Diseases Using Biometric Time-Series Data. International Journal of Multidisciplinary and Current Research, 11. https://www.researchgate.net/publication/395308897_International_Journal_of_Multidisciplinary_and_Current_Research_Deep_Learning_Framework_for_Early_Prediction_of_Chronic_Diseases_Using_Biometric_Time-Series_Data
4. Akhi, S. S., Akter, S., Hossain, M. R., Akter, A., Nobe, N., & Hosen, M. M. (2025). Early-Stage Chronic Disease Prediction Using Deep Learning: A Comparative Study of LSTM and Traditional Machine Learning Models. Frontline Medical Sciences and Pharmaceutical Journal, 5(07), 8-17. DOI: 10.37547/medical-fmspj-05-07-02, https://frontlinejournals.org/journals/index.php/fmspj/article/view/757

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.