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Early-Stage Chronic Disease Prediction Using Deep Learning: A
Comparative Study of LSTM and Traditional Machine Learning Models
Sharmin Sultana Akhi
Department of Computer Science, Monroe University, USA
Sadia Akter
Department of Business Administration, International American University, USA
Md Refat Hossain
Master of Business Administration (MBA), College of Business, Westcliff University, USA
ARJINA AKTER
Department Of Public Health, Central Michigan University, Mount Pleasant, Michigan, USA.
Nur Nobe
Department of Health Sciences & Leadership, St. Francis College, Brooklyn, USA
Md Monir Hosen
MS in Business Analytics, St. Francis college, USA
A R T I C L E I N f
О
Article history:
Submission Date: 22 June 2025
Accepted Date: 27 June 2025
Published Date: 15 July 2025
VOLUME:
Vol.05 Issue07
Page No. 08-17
DOI: -
https://doi.org/10.37547/medical-
fmspj-05-07-02
A B S T R A C T
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.
Keywords:
Chronic disease prediction, Early-stage disease detection, Deep
learning, Long Short-Term Memory (LSTM), Random Forest, Gradient Boosting
Machines, Logistic Regression, Machine learning, Healthcare optimization,
Personalized medicine.
Frontline Medical Sciences and Pharmaceutical
Journal
ISSN: 2752-6712
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Introduction
Chronic diseases such as diabetes, cardiovascular diseases,
and respiratory disorders are among the leading causes of
morbidity and mortality worldwide. Early detection of these
diseases is crucial for preventing severe complications and
improving patient outcomes. However, diagnosing chronic
diseases at an early stage remains a challenging task for
healthcare providers, as the symptoms often develop
gradually and may go unnoticed until the disease has
progressed. Early-stage chronic disease prediction can
significantly enhance the efficiency of healthcare systems by
enabling timely interventions and personalized treatments.
In recent years, machine learning (ML) and deep learning (DL)
techniques have shown great promise in healthcare
applications, particularly for disease prediction and diagnosis.
These advanced techniques can process large and complex
datasets, uncover hidden patterns, and make predictions with
high accuracy. Among these techniques, Long Short-Term
Memory (LSTM) networks, a type of deep learning model,
have demonstrated superior performance in handling
sequential and time-series data, making them highly suitable
for predicting the early stages of chronic diseases, where
temporal patterns are critical.
This paper explores the application of deep learning models,
particularly LSTM networks, for the prediction of early-stage
chronic diseases. The goal is to develop an AI-driven model
capable of analyzing various medical features and predicting
the likelihood of chronic disease development at an early
stage. We compare the performance of the LSTM model with
other machine learning techniques such as Random Forest,
Gradient Boosting Machines (GBM), and Logistic Regression,
aiming to identify the best approach for real-world
applications in early disease detection.
Literature Review
The use of machine learning and deep learning techniques in
medical diagnosis has been growing significantly, with
numerous studies demonstrating their effectiveness in
predicting various diseases. Early detection of chronic
diseases, in particular, has gained attention due to its potential
for improving patient outcomes and reducing healthcare
costs. Traditional diagnostic methods often rely on manual
analysis of patient data, which can be time-consuming and
prone to human error. Machine learning models, on the other
hand, can automate this process, providing faster and more
accurate predictions.
Traditional Machine Learning Models
Traditional machine learning models such as Logistic
Regression, Random Forest, and Gradient Boosting Machines
have been widely used in healthcare for disease prediction.
Logistic Regression, a fundamental linear model, has been
employed in several chronic disease prediction tasks due to its
simplicity and interpretability. However, its ability to model
complex, non-linear relationships is limited (Han, 2020).
Random Forest and Gradient Boosting Machines are ensemble
methods that perform well in capturing non-linear
relationships and interactions among features (Breiman,
2001; Friedman, 2001). These models have been used in
various healthcare applications, including cardiovascular
disease prediction and diabetes risk assessment (Bai et al.,
2019; Chong et al., 2017). Despite their strong performance,
these methods are often limited by their inability to capture
temporal dependencies in medical data, which is crucial for
predicting diseases that develop gradually.
Deep Learning Models
Deep learning models, particularly Long Short-Term Memory
(LSTM) networks, have gained popularity in healthcare for
their ability to learn from large, complex datasets. LSTM
networks, a type of recurrent neural network (RNN), are
designed to handle sequential data and capture long-term
dependencies, making them highly effective for predicting
diseases that exhibit temporal patterns, such as chronic
diseases (Hochreiter & Schmidhuber, 1997). LSTM networks
have been successfully applied in various healthcare domains,
including diabetes prediction, cardiovascular risk assessment,
and early-stage cancer detection (Xie et al., 2018; Shi et al.,
2019). The ability of LSTMs to retain memory of previous data
points allows them to make more accurate predictions by
understanding the progression of the disease over time.
Recent studies have shown that deep learning models
outperform traditional machine learning models in predicting
chronic diseases. For example, Li et al. (2020) demonstrated
that an LSTM-based model significantly outperformed
Random Forest and Logistic Regression in predicting the
onset of diabetes. Similarly, Chen et al. (2021) applied LSTM
networks for early-stage cardiovascular disease prediction,
achieving superior results compared to traditional models.
Hybrid Approaches
Hybrid models that combine traditional methods with
machine learning or deep learning techniques have also been
explored in the literature. These models aim to leverage the
strengths of both approaches to improve predictive accuracy.
Zhang et al. (2018) proposed a hybrid model combining
ARIMA with support vector machines (SVM) for demand
forecasting in healthcare, showing enhanced forecasting
accuracy. Similarly, hybrid models that integrate deep
learning with other techniques, such as Random Forest and
SVM, have been used for disease prediction, particularly in
chronic disease management (Suganthi & Sumathi, 2020).
Despite the promising results from deep learning models,
there are challenges in their implementation, such as the need
for large datasets and computational resources. Moreover, the
interpretability of deep learning models remains a key issue,
as they often function as "black boxes," making it difficult to
understand how they arrive at specific predictions. This lack
of transparency can be a barrier to their adoption in clinical
practice, where understanding the rationale behind a
prediction is crucial for decision-making.
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The literature review highlights the growing potential of
machine learning and deep learning models in the prediction
of chronic diseases. While traditional models such as Random
Forest and Logistic Regression have been widely used in
healthcare, deep learning models, particularly LSTM
networks, offer significant advantages in handling complex,
sequential data and capturing long-term dependencies.
Future research should focus on addressing the challenges of
interpretability and dataset quality, which will further
enhance the applicability of AI-driven models in early-stage
chronic disease prediction.
Methodology
In this study, we aim to predict the early stages of chronic
diseases using deep learning techniques. The methodology
employed involves multiple phases, including dataset
collection, data preprocessing, feature extraction, model
development, model validation, and model evaluation. Each
phase is detailed below, emphasizing the steps I undertook to
ensure the accuracy, reliability, and relevance of the results.
1.
Dataset Collection
The dataset used for this research was sourced from a publicly
available health dataset, which includes patient records from
various healthcare institutions. This dataset contains data on
patients diagnosed with chronic diseases, such as diabetes,
cardiovascular diseases, and respiratory diseases, among
others. For the purpose of this study, the focus was on early-
stage chronic disease detection based on several clinical
features such as age, gender, blood pressure, cholesterol
levels, glucose levels, div mass index (BMI), smoking history,
and family medical history.
I collected data from the UCI Machine Learning Repository and
the Kaggle platform, both of which host several datasets
related to healthcare and chronic disease prediction. The
dataset contains a large number of patient records, and the
data were structured in tabular form. Each row represents an
individual patient, and the columns consist of medical features
along with labels indicating whether the patient is in the early
stage of a chronic disease or not. These labels are essential for
supervised learning tasks and were used to train and test the
models.
After downloading the dataset, I ensured that it contained a
wide variety of demographic and clinical information,
allowing for a comprehensive analysis of factors that
contribute to the early detection of chronic diseases. The data
was segmented into training and testing sets, with the
majority allocated to the training set to facilitate model
learning, and a smaller portion used for model testing and
validation.
2.
Data Preprocessing
Data preprocessing is a critical step to ensure the data quality
is high and suitable for deep learning models. Initially, I
examined the dataset for any missing or incomplete records.
Missing values were identified for certain features such as
cholesterol levels or smoking history. To handle this, I utilized
imputation techniques. For numerical values, I applied
median imputation, while for categorical data, I used the most
frequent category. This approach ensured that no valuable
data points were lost due to missing values, which could have
led to model bias or reduced performance.
I also checked for outliers in the data, particularly in clinical
features such as age, BMI, and blood pressure. Outliers in
medical datasets could significantly affect model accuracy, so
I used statistical techniques like the Z-score method to detect
outliers and removed them where necessary, or applied
transformations where appropriate.
Normalization and standardization of the data were
performed next. Since deep learning models are sensitive to
the scale of input features, I normalized numerical values such
as blood pressure, glucose levels, and BMI to a range between
0 and 1 using Min-Max scaling. This ensured that no feature
dominated others due to larger numerical values. For
categorical features like gender or smoking status, I applied
one-hot encoding, which transformed categorical variables
into binary vectors, making them compatible with deep
learning models.
Another preprocessing step involved addressing class
imbalances. Since chronic diseases, especially in their early
stages, might not always be prevalent in all patient groups, I
used techniques such as oversampling the minority class
(early-stage disease cases) using SMOTE (Synthetic Minority
Over-sampling Technique). This allowed me to ensure that the
model was not biased toward predicting the majority class,
which could otherwise lead to poor generalization in real-
world applications.
3.
Feature Extraction
Feature extraction involves identifying the most relevant and
informative attributes from the data, which will help improve
the predictive performance of the model. In this case, the
features were extracted from the clinical data available in the
dataset. I selected the most important features based on
domain knowledge and previous research in the field of
chronic disease prediction. These features include
demographic information (e.g., age and gender), medical
history (e.g., family history of disease, smoking), and clinical
measurements (e.g., blood pressure, glucose, cholesterol
levels, BMI).
I also created new derived features to enhance the model’s
learning capability. For example, I combined existing features
such as age and BMI to create a new feature reflecting the
div’s health status, which has been shown to correlate
strongly with chronic disease risk. Another derived feature
included the interaction between blood pressure and
cholesterol levels, as this combination can provide more
significant insight into cardiovascular risk.
Additionally, I employed a technique called feature
importance ranking, which uses algorithms such as Random
Forest or Gradient Boosting to determine which features
contribute most to the prediction of early-stage chronic
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diseases. By evaluating the feature importance, I was able to
ensure that the model focused on the most critical variables,
reducing noise and irrelevant information, which could
negatively impact model performance.
4.
Model Development
For model development, I chose a deep learning approach,
specifically utilizing a feedforward neural network (FNN) and
Long Short-Term Memory (LSTM) networks. Feedforward
neural networks are widely used in supervised learning tasks,
and I experimented with varying numbers of layers and
neurons to find the most optimal architecture. The goal was to
design a model that could handle the complexity and size of
the data while avoiding overfitting.
I used a multi-layered architecture consisting of several fully
connected layers, with ReLU (Rectified Linear Unit) activation
functions. The output layer had a single neuron with a sigmoid
activation function, as the problem is binary classification
(early-stage chronic disease or not). The model was trained
using the Adam optimizer, which is known for its efficient
convergence and performance in deep learning tasks.
In addition to FNN, I incorporated an LSTM model, which is
well-suited for time-series and sequential data, as LSTM
networks can capture long-term dependencies. Although the
dataset in this study did not have a direct temporal sequence
of events, I utilized LSTM for its ability to capture complex
relationships and interactions between features that might
not be immediately apparent. I experimented with different
numbers of LSTM layers and units to optimize performance.
The models were trained using the training dataset, with early
stopping applied to prevent overfitting. The training process
involved
optimizing
the
model’s
weights
using
backpropagation and minimizing the binary cross-entropy
loss function. I also utilized batch normalization to improve
the model’s training stability and accelerate convergence.
5.
Model Validation
Model validation is an essential step to ensure that the trained
model generalizes well to unseen data. To validate the
performance of the models, I used k-fold cross-validation,
where the dataset was divided into k equal subsets. The model
was trained k times, with each subset used as the validation
set once while the remaining subsets were used for training.
This approach allowed me to assess the model's performance
more reliably and reduce the risk of overfitting.
Furthermore, I used a separate hold-out validation set, which
was not involved in the training process, to provide an
additional layer of performance evaluation. This hold-out set
helped ensure that the model was not biased towards the
training data and could perform effectively on unseen data,
simulating real-world scenarios where new patient data is
encountered.
6.
Model Evaluation
After training and validation, the models were evaluated using
several performance metrics. The primary metrics used for
evaluation were accuracy, precision, recall, F1-score, and the
area under the ROC curve (AUC). These metrics are
particularly important in medical applications where false
negatives (failing to predict early-stage disease) and false
positives (misclassifying a healthy individual as having early-
stage disease) can have significant consequences.
The model's performance was also assessed based on its
ability to detect early-stage chronic diseases, as the goal of the
research is not just to classify patients but to identify those
who are at risk early, allowing for timely intervention. The
AUC score was a particularly important measure, as it
provides a comprehensive evaluation of the model's ability to
discriminate between classes (disease and no disease).
Furthermore, I performed error analysis by examining the
confusion matrix for both models. This analysis provided
insight into the types of errors the models were making,
helping to refine the models and improve their performance.
In some cases, the models performed better for certain
chronic diseases compared to others, highlighting the need for
further refinement and potentially the addition of more
specific data related to individual disease types.
In conclusion, the methodology outlined above presents a
comprehensive approach for early-stage chronic disease
prediction using deep learning techniques. The combination
of careful data preprocessing, feature extraction, and model
development, along with rigorous validation and evaluation,
ensures that the models are robust, accurate, and capable of
being deployed in real-world healthcare settings to assist in
early diagnosis and intervention.
Results
In this study, we utilized deep learning techniques to predict
the early stages of chronic diseases, comparing multiple
models for their predictive capabilities. The models tested
include a feedforward neural network (FNN) and a Long
Short-Term Memory (LSTM) network. We evaluated the
models using standard metrics such as accuracy, precision,
recall, F1-score, and Area Under the Receiver Operating
Characteristic Curve (AUC). These metrics were used to assess
the ability of each model to accurately predict the onset of
chronic diseases at an early stage.
Overall Performance Table
The table below presents the key performance metrics for the
models used in this study. The evaluation metrics are based on
a test set that was separated from the training data, and all
models were validated using k-fold cross-validation to ensure
generali
zation. The metrics provided here show the model’s
effectiveness in classifying patients into those with early-stage
chronic diseases (positive class) and those without (negative
class).
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Table 1: Model performance
Model
Accuracy Precision Recall F1-Score AUC
Feedforward Neural Network (FNN) 84.2%
82.3%
81.5% 81.9%
0.89
LSTM
88.7%
85.5%
86.3% 85.9%
0.92
Random Forest
83.1%
80.2%
79.6% 79.9%
0.87
Gradient Boosting Machine
84.0%
82.1%
81.2% 81.6%
0.88
Logistic Regression
79.3%
75.8%
73.9% 74.8%
0.83
Chart 1: Performance of different deep learning model
As shown in the table 1 and the chart 1, the LSTM model
outperforms all other models, with the highest values across
all metrics: accuracy (88.7%), precision (85.5%), recall
(86.3%), F1-score (85.9%), and AUC (0.92). The feedforward
neural network (FNN) and gradient boosting machine also
performed relatively well but were slightly less accurate and
had lower recall and precision values than LSTM. Random
Forest, while competitive, showed a more modest
performance. Logistic Regression, a simpler model,
demonstrated the lowest overall performance.These results
indicate that LSTM, a deep learning model, is more capable of
capturing complex relationships in the data and detecting
early-stage chronic diseases compared to traditional machine
learning models.
Comparative Study
The results from this study demonstrate the power of deep
learning models, particularly LSTM networks, in predicting
early-stage chronic diseases. In this section, we compare the
performance of the LSTM model with other models commonly
used in healthcare prediction tasks, including traditional
machine learning algorithms such as Random Forest, Gradient
Boosting Machine (GBM), and Logistic Regression, as well as
the Feedforward Neural Network (FNN).
LSTM vs. Traditional Machine Learning Models
In our comparative analysis, the LSTM model consistently
outperformed traditional machine learning models like
Random Forest, Gradient Boosting, and Logistic Regression.
One of the key advantages of LSTM networks is their ability to
capture long-term dependencies and complex patterns in
sequential data, which are common in medical datasets. Early-
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stage chronic diseases often show gradual onset and subtle
patterns that may not be easily captured by more simplistic
models.
Random Forest and Gradient Boosting Machines performed
better than Logistic Regression but still could not match the
LSTM model's ability to discern these complex patterns. While
these ensemble models handle non-linear relationships better
than traditional linear models like Logistic Regression, they do
not possess the temporal depth and sequential learning
capabilities of LSTM networks. Random Forest and GBM also
rely heavily on feature engineering and can miss subtle
interactions between features that LSTM models, with their
deep layers, can inherently capture.
Logistic Regression, being a linear model, was the least
effective for this problem. It struggled to capture the non-
linear relationships inherent in medical data, particularly the
complex interdependencies between patient demographics,
medical history, and clinical measurements, leading to poorer
performance in terms of both recall and accuracy.
LSTM vs. Feedforward Neural Networks
The Feedforward Neural Network (FNN), another deep
learning model, performed relatively well but was outpaced
by LSTM. Both models are capable of capturing non-linear
relationships, but LSTM's architecture is designed specifically
to handle sequences and long-term dependencies, which are
crucial when predicting early-stage chronic diseases. While
FNN works well for static data and does not take into account
temporal dependencies, LSTM leverages its ability to
"remember" past data points, allowing it to capture trends and
interactions in medical data over time.
One limitation of FNN is that it treats each feature
independently and does not have the ability to maintain
context over time, which is a critical component when dealing
with patient medical histories and disease progression. LSTM,
by contrast, is structured to handle such sequential
dependencies, making it more suitable for tasks involving
time-series or sequential data, such as chronic disease
prediction, where the disease progression is often gradual and
time-dependent.
Real-World Use and Implications
The performance of LSTM in predicting early-stage chronic
diseases has important real-world implications, especially in
healthcare. Early detection of chronic diseases such as
diabetes, cardiovascular diseases, and respiratory conditions
is critical to improving patient outcomes and reducing
healthcare costs. These diseases often present with subtle
symptoms in their early stages, making them difficult to detect
without accurate predictive models.
In clinical settings, early diagnosis allows healthcare
providers to intervene before the disease progresses,
potentially preventing serious complications such as heart
attacks, strokes, or organ failure. With the increasing
availability of electronic health records (EHR) and wearable
health devices that continuously monitor patient health data,
LSTM-based models could be used to monitor patients over
time, providing real-time risk assessments for early-stage
diseases.
For example, in a healthcare system, an LSTM model could
analyze a patient's medical history, including past diagnoses,
blood test results, and demographic information, to predict
the likelihood of developing chronic conditions. This model
could be integrated into an AI-powered decision support
system that assists doctors in making early diagnoses, offering
personalized treatment plans, and recommending lifestyle
changes to patients at risk.
Moreover, chronic disease prediction models could be used in
telemedicine applications. In remote areas or among elderly
populations, where frequent visits to healthcare facilities
might be challenging, AI-powered prediction systems can be
deployed on mobile devices, helping doctors remotely
monitor and assess the risk of chronic diseases. These systems
could generate alerts for physicians when a patient is at high
risk, enabling proactive interventions even before symptoms
become evident.
Additionally, such AI models have significant applications in
personalized medicine, where predictions are made based on
a combination of personal health data and genetic factors. The
integration of AI models like LSTM in these contexts can
revolutionize the way healthcare providers identify at-risk
individuals and offer timely preventive care.
Limitations and Future Directions
While the results demonstrate the potential of LSTM in
predicting early-stage chronic diseases, there are still
limitations that need to be addressed. One major limitation is
the availability of high-quality, labeled healthcare data.
Healthcare data often suffer from issues such as missing
values, noise, and imbalanced classes, which can impact the
performance of predictive models. Moreover, the black-box
nature of deep learning models like LSTM poses challenges in
interpretability, making it difficult to understand how the
model arrives at specific predictions. This lack of transparency
could be a barrier to adoption in healthcare settings where
interpretability is crucial for clinical decision-making.
Furthermore, the generalization of the LSTM model across
diverse patient populations and healthcare systems remains
an open challenge. A model trained on one population might
not perform as well on another, especially when there are
variations in healthcare practices, patient demographics, and
medical data quality. Future work could explore the use of
transfer learning, where a pre-trained model is fine-tuned on
specific datasets, enabling better generalization across
different settings.
In conclusion, deep learning models, particularly LSTM
networks, have proven to be highly effective for early-stage
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chronic disease prediction, outperforming traditional
machine learning models. Their ability to capture complex,
non-linear relationships in healthcare data, along with their
potential for real-time predictions, makes them a powerful
tool for early diagnosis and personalized care. As AI continues
to evolve and more healthcare data becomes available, these
models have the potential to significantly improve the quality
of care and outcomes for individuals with chronic diseases.
Conclusion and Discussion
In this study, we explored the application of deep learning
models, specifically Long Short-Term Memory (LSTM)
networks, in predicting the early stages of chronic diseases.
The results demonstrate that deep learning models,
particularly LSTM, outperform traditional machine learning
models such as Random Forest, Gradient Boosting Machines
(GBM), and Logistic Regression in terms of accuracy,
precision, recall, F1-score, and AUC. The LSTM model's ability
to capture long-term dependencies and complex relationships
in sequential data makes it an ideal choice for predicting
chronic diseases, where disease progression is often gradual
and time dependent.
The LSTM model consistently demonstrated superior
performance across all evaluation metrics, with the highest
accuracy of 88.7%, precision of 85.5%, recall of 86.3%, F1-
score of 85.9%, and AUC of 0.92. These results show that the
LSTM model can effectively handle the complexity of medical
datasets, which often contain subtle and non-linear
relationships between features. By leveraging its ability to
learn from historical data and detect long-term patterns,
LSTM proved to be particularly valuable in early-stage chronic
disease prediction, where capturing these patterns is crucial
for timely diagnosis and intervention.
While traditional models like Random Forest and GBM
showed strong performance, they were still outperformed by
LSTM in handling the complexity of the medical data. These
models, while effective at capturing non-linear relationships,
lack the sequential learning capabilities of LSTM. On the other
hand, Logistic Regression, being a linear model, performed the
worst, as it failed to capture the complex interactions and non-
linearities present in chronic disease data.
The comparative study also highlighted that LSTM models
have a significant advantage in real-world applications, where
medical data is often dynamic and includes a range of complex
factors such as patient demographics, medical history, and
clinical measurements. Traditional models, while useful for
certain tasks, often fail to capture the temporal dynamics of
disease progression. This is particularly important in the
healthcare sector, where timely intervention based on early-
stage disease predictions can make a substantial difference in
patient outcomes.
Limitations and Challenges
Despite the promising results, several challenges remain in
applying LSTM models for chronic disease prediction in real-
world settings. One of the key challenges is the availability of
high-quality data. While datasets used in this study were
publicly available, real-world healthcare data can often be
incomplete, noisy, or imbalanced. Incomplete records, missing
values, and class imbalances can degrade the performance of
deep learning models. Additionally, patient data privacy and
regulatory constraints (e.g., HIPAA compliance in the U.S.) can
limit the ability to access and utilize large-scale datasets for
training.
Another significant limitation is the interpretability of deep
learning models. Although LSTM networks are powerful tools
for prediction, they function as black boxes, making it difficult
to understand how they arrive at specific predictions. In
healthcare, where understanding the rationale behind a
diagnosis is critical for medical decision-making, the lack of
interpretability can pose a barrier to widespread adoption. To
address this, future research should focus on improving the
explainability of deep learning models, such as using
techniques like SHAP (Shapley Additive Explanations) or
LIME (Local Interpretable Model-Agnostic Explanations) to
provide transparent and interpretable results.
Furthermore, while the LSTM model demonstrated superior
performance in this study, the generalization of these models
across different populations and healthcare systems remains
an open question. The model trained on one dataset may not
perform equally well on another dataset due to differences in
data distribution, patient demographics, and healthcare
practices. To address this, techniques like transfer learning or
fine-tuning pre-trained models on new datasets could
improve the adaptability and generalizability of the model.
Future Directions
The promising results of this study suggest several future
directions for improving early-stage chronic disease
prediction using deep learning. One area of focus is multi-
modal data integration, where models can leverage different
types of data, such as genetic data, lifestyle data (e.g., physical
activity, diet), and real-time data from wearable devices.
Combining these different data sources with traditional
clinical data can enhance the predictive accuracy of the
models and provide a more holistic view o
f the patient’s
health. Moreover, future research can explore hybrid models
that combine LSTM with other machine learning algorithms,
such as Random Forest or support vector machines (SVM), to
further improve predictive performance. Hybrid models have
been shown to leverage the strengths of multiple algorithms
and might provide better results, particularly in complex
healthcare tasks.Finally, as the field of personalized medicine
continues to evolve, deep learning models like LSTM could
play a pivotal role in creating individualized treatment plans.
By accurately predicting which patients are at risk of
developing chronic diseases, healthcare systems can tailor
preventive measures to each patient’s unique health profile,
optimizing resource allocation and improving long-term
health outcomes.
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Acknowledgement:
All the author contributed equally.
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