Description:FIELD OF THE INVENTION
The present invention relates to the application of machine learning (ML) techniques in healthcare, specifically for predicting early readmission of diabetic patients. The invention leverages advanced ML models such as logistic regression, decision trees, random forests, gradient boosting, and artificial neural networks (ANN) to improve patient outcomes by enabling early intervention and personalized treatment strategies.
BACKGROUND OF THE INVENTION
The proposed system represents a forward-looking and innovative approach to the management of hyperglycaemia in chronic disease patients. It introduces a comprehensive framework that integrate advanced Machine Learning (ML) models to enable early prediction, personalized interventions and improved patient outcomes.
Traditional healthcare practices suffer from several challenges in the effective management of chronic diseases. They rely on conventional methods of patient monitoring and intervention, which may lack the precision and predictive capabilities required for optimal outcomes. The current methods do not predict hyperglycaemia. This may result in delayed interventions and suboptimal patient outcomes. The utilization of extensive clinical databases is often limited. The lack of advanced data analysis techniques hinders the extraction of insights associated with hyperglycaemia. The current systems may rely on standardized treatment protocols overlooking the individual nature of patient conditions. These challenges are overcome by the proposed solution.
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 proposed invention presents an innovative framework for predicting early readmission of diabetic patients using ML models. The system employs predictive modeling techniques to analyze patient-specific parameters such as demographics, disease severity, insulin intake, number of medications, and duration of hospitalization.
The predictive framework is structured in multiple phases. The first phase involves preprocessing patient data to address missing values and standardize features. The second phase focuses on feature scaling and splitting data into training and testing sets. The third phase applies various ML models to classify patients based on their likelihood of readmission.
A key aspect of the invention is the integration of an artificial neural network (ANN) using TensorFlow/Keras, which enhances the predictive capability of the system. The ANN model is trained using extensive clinical datasets and optimized through hyperparameter tuning and cross-validation. This ensures high accuracy and robustness in predicting hyperglycemia-related readmissions.
To facilitate real-time decision-making, a graphical user interface (GUI) is developed. The GUI allows healthcare professionals to input patient parameters and receive immediate predictions on the likelihood of readmission. The system provides actionable insights, enabling timely interventions and personalized treatment plans.
By combining traditional ML models with deep learning techniques, the invention significantly improves the accuracy of readmission predictions. The system enhances patient care, reduces healthcare costs, and enables data-driven decision-making for chronic disease management.
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 work analysis involves the extraction of detailed information to conduct a thorough investigation, utilizing various data mining techniques to explore the relationship between early admissions. The study controls disease severity, demographics, type of admission and disease type.
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: HYPERGLYCAEMIA PREDICTIVE MODELING SYSTEM ARCHITECTURE FIGURE 2: HYPERGLYCAEMIA ACTIVITY DIAGRAM
FIGURE 3: SEQUENCE DIAGRAM
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 work analysis involves the extraction of detailed information to conduct a thorough investigation, utilizing various data mining techniques to explore the relationship between early admissions. The study controls disease severity, demographics, type of admission and disease type.
The goal was to enhance patient safety by developing effective predictive model and analyzing factors contributing to hyperglycaemia. The implementation involved various ML models, including various ML models, including logistic regression, decision tree, random forest, gradient boosting and Artificial Neural Network (ANN).
The system framework consists of multiple components, including data preprocessing, feature selection, model training, and predictive analysis. The data preprocessing module handles missing values, normalizes data, and performs feature engineering to improve model accuracy. The feature selection process identifies the most relevant patient attributes that contribute to readmission risks.
The machine learning models implemented in the system include logistic regression, decision trees, random forests, and gradient boosting. Each model undergoes hyperparameter tuning to optimize performance. The ANN model, developed using TensorFlow/Keras, introduces a deep learning approach that enhances predictive accuracy.
The system workflow begins with data acquisition from electronic health records (EHRs) and patient databases. The data undergoes preprocessing before being fed into the ML models for classification. The results are analyzed, compared, and validated using cross-validation techniques.
The real-time GUI enables healthcare professionals to enter patient details such as age, gender, time in hospital, lab test results, and medication history. The system processes these inputs and generates predictions on whether the patient is at risk of early readmission. The decision-making module provides recommendations based on model outputs, guiding clinicians in treatment planning.
The cloud-based architecture allows for scalability and integration with hospital management systems. Data security protocols ensure patient information remains confidential and protected. The system continuously learns from new patient data, refining its predictive models for improved accuracy over time.
In the proposed system, the ML models are tailored for binary classification tasks, aiming to predict hyperglycemia in patients. This is the first step, namely, Predictive Modeling with ML. The second phase of the work is Hyperparameter Tuning and Cross-Validation. The task is to compare the models. Then ANN is introduced with TensorFlow/keras. Then analysis is made.
The qualities of the dataset on hospital readmission are: Category, age, sex, insulin intake, no. of medications, no. of laboratory, time in hospital, admission type and discharge type.
In the work flow, to address the missing data, preprocessing is done. Then in the feature scaling, training and testing data are split. All ML models are worked out to get the performance.
The introduction of ANN using TensorFlow/keras added a modern and powerful dimension to the analysis. ANN with its architecture, demonstrated competitive performance compared to traditional models. The comparison between traditional ML models and the ANN provides a basis for understanding their respective roles and effectiveness in predicting hyperglycemia.
Furthermore, we create graphical user interface that is readmission model which takes the feature values from user and predict whether the patient will be readmitted or not.
Examples of feature values are: gender, age, time in hospital in days, no. of lab procedures etc.
Among the chronic conditions, effective management of hyperglycaemia stands out due to its significant impact on patient outcomes, encompassing mortality and morbidity. To address this issue, predictive modelling has emerged as a promising avenue for revolutionizing healthcare practices, particularly in decision-making and data analysis.
ADVANTAGES OF THE INVENTION
The readmission rate of hospitalized diabetic patients is considerably high. So, in this scenario,
this predictive modeling for chronic diseases holds transformative potential in revolutionizing
healthcare practices, specifically in decision-making and data analysis.
This work not only assists healthcare professionals and decision-makers but also has the potential to inform improved strategies for managing hyperglycemia and enhancing patient outcomes.
, Claims:1. A machine learning-based system for predicting early readmission of diabetic patients, comprising:
A data preprocessing module for handling missing values and normalizing patient data;
A machine learning model selection module implementing logistic regression, decision trees, random forests, gradient boosting, and artificial neural networks;
A hyperparameter tuning module for optimizing model performance;
A graphical user interface (GUI) for real-time prediction of patient readmission risks;
A cloud-based infrastructure for data storage, processing, and integration with hospital management systems.
2. The system as claimed in claim 1, wherein the data preprocessing module includes feature selection techniques to improve model accuracy.
3. The system as claimed in claim 1, wherein the artificial neural network is implemented using TensorFlow/Keras for enhanced predictive modeling.
4. The system as claimed in claim 1, wherein hyperparameter tuning and cross-validation are applied to optimize machine learning model performance.
5. The system as claimed in claim 1, wherein real-time patient data can be input into the GUI for immediate readmission risk assessment.
6. The system as claimed in claim 1, wherein the system provides decision support recommendations based on predictive analysis results.
7. The system as claimed in claim 1, wherein the cloud-based infrastructure ensures secure data handling and scalability.
8. The system as claimed in claim 1, wherein predictive model accuracy is continuously improved through iterative learning from patient data.
9. The system as claimed in claim 1, wherein multiple machine learning models are compared and selected based on performance metrics.
10. The system as claimed in claim 1, wherein encrypted communication protocols ensure patient data privacy and security.