Description:FIELD OF THE INVENTION
The present invention relates to artificial intelligence applications in medical diagnostics. More particularly, it pertains to a hybrid deep learning framework that integrates multi-head attention mechanisms with stacked Long Short-Term Memory (LSTM) networks for heart disease prediction, offering enhanced accuracy, interpretability, and clinical usability in real-world decision-support systems.
BACKGROUND OF THE INVENTION
Heart disease remains one of the leading causes of mortality worldwide, making early and accurate prediction essential for effective clinical intervention. Traditional machine learning models often struggle to capture complex temporal dependencies and inter-feature relationships present in medical data, leading to suboptimal prediction accuracy. While deep learning models like LSTM can process sequential data, they may overlook the varying importance of features across time steps. Additionally, existing models often lack interpretability and generalization when applied to real-world, multi-dimensional clinical datasets.
To address these limitations, there is a pressing need for a robust hybrid deep learning framework that can enhance prediction performance by integrating both temporal and contextual feature attention. This research proposes a novel approach that combines Multi-Head Attention mechanisms with Stacked Long Short-Term Memory (LSTM) networks to effectively model intricate patterns and dependencies in heart disease datasets, ultimately improving prediction accuracy, interpretability, and clinical applicability.
US20220304585A1: The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of Poincaré map of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.
US11864944B2: A method for determining a predicted risk level of a clinical endpoint for a predetermined time period for a patient is provided by the present disclosure. The method includes receiving video frames of a heart, the video frames being associated with the patient, receiving electronic health record data including a number of variables associated with the patient, providing the video frames and the electronic health record data to the trained neural network, receiving a risk score from the trained neural network, and outputting a report based on the risk score to at least one of a display or a memory.
Heart disease remains a leading cause of death worldwide, demanding early and accurate detection to save lives. Traditional predictive models, including conventional machine learning and even basic deep learning approaches, struggle to capture long-term temporal dependencies and complex inter-feature relationships inherent in medical records. These models often treat all features equally, resulting in suboptimal accuracy. Furthermore, most existing systems lack interpretability, which limits trust and clinical adoption.
The present invention solves these problems by introducing a hybrid deep learning framework that combines multi-head attention with stacked LSTM layers. The attention mechanism dynamically identifies critical risk factors, while LSTM networks capture sequential dependencies across patient records. Together, they provide more accurate predictions and a transparent framework that highlights significant clinical parameters, making it suitable for clinical decision support systems (CDSS).
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention discloses a hybrid framework for predicting heart disease using deep learning. The system integrates multi-head attention with stacked LSTM networks to simultaneously model temporal and contextual aspects of patient data. The multi-head attention layer identifies and weighs the most significant risk factors such as blood pressure, cholesterol, and ECG patterns. The stacked LSTM layers capture long-term dependencies in sequential medical records, ensuring that important temporal patterns are not overlooked.
The system is trained on multidimensional clinical datasets and incorporates regularization techniques to prevent overfitting. It further includes interpretability components that allow clinicians to visualize which features influenced a prediction, thereby building trust in the model’s recommendations.
Unlike traditional models that rely heavily on manual feature selection or shallow statistical methods, the invention provides a fully automated and adaptive framework. It is designed to operate in real time, making it suitable for clinical environments where rapid and accurate decision-making is critical.
The invention achieves improved predictive performance, offers feature-level insights, and is adaptable across diverse patient datasets. This makes it highly effective for integration into CDSS platforms for diagnosis, prognosis, and management of heart disease.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The innovation suggests using a hybrid framework to enhance the reliability and accuracy of determining heart disease. The model uses Multi-Head Attention Mechanisms working together with Stacked Long Short-Term Memory networks in order to see which features are important and how the data progresses over time in patient records.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The innovation suggests using a hybrid framework to enhance the reliability and accuracy of determining heart disease. The model uses Multi-Head Attention Mechanisms working together with Stacked Long Short-Term Memory networks in order to see which features are important and how the data progresses over time in patient records.
The Multi-Head Attention component makes it possible for the model to attend to various features at the same time, which helps it pay more attention to the most significant risk factors involved in heart disease. At the same time, the Stacked LSTM layers deal with the sequence of the data, remembering crucial dependencies for a long time and bringing out patterns that emerge gradually.
Further, applying regularization techniques keeps the system from being overly complex, while clearly understanding significant parameters aids the decision-making process in clinical settings. The model was developed by training on medical datasets and showed better results than traditional and standalone deep learning methods.
The new invention offers more precise results and a solution that is easy to understand and use in real-time CDSS systems for quick diagnosis and management of heart disease.
A hybrid technique that brings together stacked recurrent units and multi-head attention to boost the features and predictive importance of heart disease diagnosis compared to common methods.
The invention provides a hybrid deep learning framework for predicting heart disease based on clinical and sequential patient data. The framework integrates a multi-head attention mechanism with stacked LSTM networks to capture both contextual feature importance and temporal dependencies.
Patient data often includes multiple variables such as blood pressure, cholesterol levels, age, smoking status, ECG measurements, and family history. Traditional approaches struggle to interpret complex interrelationships among these features. The invention overcomes this challenge by embedding a multi-head attention layer that identifies and emphasizes clinically significant attributes in the dataset. Each attention head focuses on different aspects of the data, ensuring a comprehensive representation of feature interactions.
The stacked LSTM component of the framework processes the data sequentially. LSTM cells are well-suited for modeling temporal dependencies by retaining information over long sequences. In the present invention, multiple LSTM layers are stacked to capture hierarchical temporal relationships. This allows the model to detect subtle changes in patient records over time, which may indicate the onset or progression of heart disease.
The combination of multi-head attention and stacked LSTM layers enables the system to learn both short-term fluctuations and long-term trends in medical records. For example, the framework can identify how a small but persistent rise in blood pressure interacts with cholesterol levels to indicate elevated cardiovascular risk.
The invention includes preprocessing modules to clean and normalize input data. Missing values are handled through imputation, and categorical features are encoded numerically. Continuous features are standardized to ensure uniform scale, improving the performance of the deep learning model.
The training process involves optimizing the model on large clinical datasets. The system uses regularization methods such as dropout and weight decay to prevent overfitting, ensuring robust performance across unseen patient data. Hyperparameters including learning rate, number of LSTM layers, and attention heads are fine-tuned through cross-validation.
Interpretability is a core aspect of the invention. The framework incorporates visualization tools that allow clinicians to see which features and time steps contributed most to a prediction. This provides transparency and builds trust, making the model more acceptable for use in clinical decision-making.
The invention can be deployed in both real-time and offline modes. In real-time deployment, patient data is processed continuously, enabling timely predictions in emergency or monitoring settings. Offline deployment allows retrospective analysis of historical patient records for research or long-term prognosis.
The system architecture is modular, making it compatible with electronic health record (EHR) systems. It can be integrated into existing CDSS platforms and used alongside clinical workflows. The invention is scalable, capable of processing small datasets for individual clinics or large-scale data from hospital networks.
In addition to prediction accuracy, the invention emphasizes interpretability and clinical relevance. Unlike black-box models, the framework highlights the importance of features such as ECG changes, family history, or lifestyle factors in driving predictions. This aligns with clinicians’ need for models that support, rather than replace, their expertise.
The invention can also be adapted to predict other cardiovascular conditions beyond heart disease, including arrhythmias, hypertension-related complications, and stroke risk, by retraining the model with appropriate datasets.
Through its hybrid architecture, the invention addresses the shortcomings of conventional models and provides a practical, accurate, and interpretable solution for heart disease prediction in modern healthcare.
Best Method of Working
The best method of working involves training the hybrid model on a large, well-annotated clinical dataset that includes both sequential and static features of patients. Data preprocessing modules should normalize continuous variables, encode categorical data, and handle missing values appropriately. The model should employ stacked LSTM layers with at least two or more layers, combined with a multi-head attention mechanism comprising multiple attention heads. Dropout layers should be included for regularization. The system should be fine-tuned through cross-validation, and its interpretability module should provide feature attribution maps for clinical decision support. Deployment is best achieved through integration into hospital EHR systems, allowing real-time monitoring and prediction of patient heart disease risk.
, Claims:1. A hybrid deep learning system for heart disease prediction comprising:
a preprocessing module configured to normalize, encode, and handle missing values in patient data;
a multi-head attention module configured to identify and weigh significant clinical features;
a stacked long short-term memory module configured to capture temporal dependencies across patient records;
a regularization module configured to prevent overfitting through dropout and weight decay; and
an interpretability module configured to display feature-level contributions to predictions,
wherein the system provides accurate and interpretable heart disease prediction.
2. The system as claimed in claim 1, wherein the multi-head attention module comprises multiple attention heads focusing on different clinical features.
3. The system as claimed in claim 1, wherein the stacked long short-term memory module comprises two or more LSTM layers arranged hierarchically.
4. The system as claimed in claim 1, wherein the preprocessing module standardizes continuous variables and encodes categorical variables.
5. The system as claimed in claim 1, wherein the interpretability module generates visualization of feature importance for clinical decision support.
6. A method for predicting heart disease using a hybrid deep learning framework, the method comprising:
preprocessing patient data by normalization, encoding, and handling missing values;
extracting feature importance using a multi-head attention module;
capturing temporal dependencies through a stacked long short-term memory network;
classifying the patient as at risk or not at risk of heart disease; and
providing interpretability outputs that identify significant features contributing to the prediction.
7. The method as claimed in claim 6, wherein the multi-head attention module assigns different weights to features including blood pressure, cholesterol, and ECG measurements.
8. The method as claimed in claim 6, wherein the stacked long short-term memory network models sequential dependencies in patient records over multiple visits.
9. The method as claimed in claim 6, wherein regularization techniques are applied to improve generalization across diverse datasets.
10. The method as claimed in claim 6, wherein the method is integrated with electronic health records for real-time clinical decision support.