Abstract
Welcome to the Special Issue of Advances in Computational Design (ACD) devoted to "Towards an Inclusive Privacy Framework for Electronic Health Record Systems: Integrating Ontology and Machine Learning Techniques"
The quick shift to digital healthcare systems has created new chances to improve patient care, diagnosis, and medical research. However, this change has also triggered serious concerns about the privacy, security, and interoperability of Electronic Health Records (EHRs). As healthcare data grows more complex and connected, we need smart frameworks that can ensure accessibility while keeping information confidential.
This special issue, "Towards an Inclusive Privacy Framework for Electronic Health Record Systems: Integrating Ontology and Machine Learning Techniques", brings together research that connects healthcare informatics, artificial intelligence, and data governance. The contributions in this issue investigate new ontology-driven methods for organizing medical data, machine learning models for secure data sharing and anomaly detection, and privacy-preserving techniques to maintain patient trust and comply with global standards.
The papers emphasize the importance of inclusive and ethical AI design in creating sustainable healthcare systems. By combining semantic interoperability, adaptive learning models, and robust privacy frameworks, this issue aims to push forward the best practices in secure health data management. We thank all authors, reviewers, and editorial members for their valuable contributions to this impactful compilation.
Key Words
Address
P. Raviraj: Dept. of Computer Science & Engineering GSSS Institute of Engineering and Technology for Women, K R S Road, Metagalli, Mysuru – 570016, Karnataka State, India
Jingshan Huang: School of Computing, College of Medicine, University of South Alabama, 307 N University Blvd, United States
Maode Ma: KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar
Abstract
This study proposed an Attention Algorithm Based-Random Forest model (AAB-RF) To enhance the precision of the ML model, dataset 1 focuses on cardiac disease, including metrics like age, cholesterol, blood pressure, and other cardiovascular factors. The data set 2 aims at the mitral valve issues, especially focused on detecting the prosthetic valve anomalies. The two datasets are analyzed using machine learning classifiers, including K-Nearest Neighbors, Random Forest, Naive Bayes, Logistic Regression and advanced methods like Convolutional Neural Networks and a VGG-based framework. When analyzing the data set 1, the proposed AAB-RF model achieves the classification accuracy of 93% performs better than the other models like Naive Bayes of 88.52% and Support Vector Machine of 89% accuracy. Likewise, for data set 2, the proposed AAB-RF model reached the remarkable accuracy of 99.40% performs superior than CNN and VGG achieved an accuracy of 97% and 84% respectively whereas closely matching with the Vafaeezadeh et al. (2021) model's accuracy of 99.00%. The major advantage of integrating attention mechanism with the Random forest model enhances the feature selection and decision making, especially in datasets having the sophisticated interdependent nature. This study showcasing the AAB-RF model's efficiency in managing the multiple datasets which enables the robust and effective outcomes. The research outcomes shows its effectiveness which guides the physicians in diagnosing the heart disease and interpreting the mitral valve features with high accuracy and reliability.
Abstract
The biomedical industry uses graph mining to store a variety of data. It features a feature that makes a lot of info accessible. However, the majority of individuals are mainly concerned with learning about illnesses and medications. Creating drug models and novel chemical molecules in the medical industry is called relational medicine. Patients may have adverse reactions to the medication due to the dissimilarity of the compound's molecules. It suggests that complex molecular characteristics don't affect the dataset's classification and are independent of one another. We aim to increase the effectiveness of graph mining-based drug selection by analyzing the success rate of drug selection using Fuzzy Multilayer Neural Perception (FMNP). Marginal Subset Clustering Features (MSCF), the input used to generate the chosen classes, are processed utilizing these features. The system first preprocessed all patient features and suggested medication molecule compounds to create a consolidated dataset. Distance vector features linked to edge weights are used in feature selection to create relationship patterns. The features are chosen based on the estimated Relational Drug Combination Weight (RDCW). Additionally, the implementation updates the logic rules and gives the neural classifier predictions for feature weights. Using a neural classifier and iterative logic rules, FMNP forecasts training outcomes. The classifier continuously predicts and suggests chemical molecules to lower the possibility of adversative effects.
Key Words
drug selection; feature weights; medicatio; neural classifier; RDCW; selection and fuzzy
Address
P. India Solai and G. Srinaganya: Department of Computer Science, National College (Autonomous), Affiliated to Bharathidasan University, Trichy - 620001
Abstract
Numerous disorders related to lifestyle choices and environmental factors are prevalent among humans today. Predicting and detecting these diseases early on is essential to halting their spread and severity. For physicians, accurately diagnosing illnesses can be challenging. Specifically, one of the key origins of morbidity and death from non-communicable diseases that impact 10-15% of the global population is chronic kidney disease, or CKD. Still, making medical predictions is a difficult and complex undertaking. Our proposed system uses powerful machine learning algorithms to detect and predict people with prevalent chronic conditions. These methods can enhance classifiers' ability to reliably identify chronic diseases. The dataset collected from Kaggle is a chronic kidney disease dataset, comprising 25 features. The first step is preprocessing and normalization of the dataset. PCA extracts the features of chronic disease. The k-nearest neighbour (KNN) is a feature selection method used to select features. A CNN (convolutional neural network)-GRU (gated recurrent unit) classification algorithm is used to predict disease from the dataset. The predicted result is binary, like "CKD" or "NOT CKD", The classification algorithm efficiently evaluates performance metrics, including precision, accuracy, recall, and an F1 score of 1.0.
Address
Shiju K Binu and R. Devi: School of Computing Sciences, Vels Institute of Science Technology & Advanced Studies Chennai, Tamil Nadu 600117, India
Abstract
Neuroblastoma is the most common extracranial solid malignancy in children. It is possible to estimate the cancer's unpredictable biological activity based on the patient's age, genetic makeup, the biology of the cancer, and the extent of disease at diagnosis. Machine learning algorithms have the potential to enhance the precision and efficacy of cancer diagnosis, individualized therapy selection, and long-term outcome prediction. A subset of machine learning known as Artificial Intelligence (AI) is capable of spotting patterns in data and acting without the need for special programming to accomplish predetermined objectives. The patient populations most likely to benefit from advanced imaging tests may be enriched, high-risk populations can be identified, and individualized screening tests can be prescribed with the aid of machine learning technologies. Modern computational tools are becoming more and more crucial in pediatric oncology because of their invasive nature and the requirement for an early and precise diagnosis. Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) are two methods that can be used to increase the precision and predictability of detection. The Grey-Level Cooccurrence Matrix (GLCM) extracts the features as energy, entropy, dissimilarity, homogeneity, and contrast. The CNN-GRU to produce the results as precision, recall, accuracy, and F1-score 93%, 80%, 95%, and 83%.
Abstract
Breast cancer is a major health issue, and effective treatment depends on a prompt diagnosis. Particularly mammography is important in the detection of breast cancer. Deep learning algorithms have shown promise in analyzing medical images, but their performance heavily relies on large labeled datasets, which are often limited in the context of breast cancer. In spite of the lack of labeled data, this study suggests a unique method called Cross-Dimensional Transfer Learning to increase the precision of cancer identification using deep learning. The method utilizes multiple imaging modalities, such as mammography and ultrasound, to leverage the complementary information and transfer knowledge learned from one modality to enhance classification performance on another. The proposed work consists of following three phases: Pretraining on Diverse Data, Modality-Specific Fine-Tuning and Cross-Dimensional Transfer Learning. A deep learning model is pretrained on a diverse dataset that includes breast cancer images from different modalities. This phase enables the model to learn general features and representations applicable across various imaging modalities. After pretraining, the model is perfected using labeled data specific to each modality. This process enables the model to adapt its learned features to the exclusive features of each imaging modality, improving its ability to capture modality-specific patterns related to breast cancer. Once modality-specific fine-tuning is complete, knowledge acquired from one modality is transferred to another by leveraging shared representations between the imaging modalities. This transfer of knowledge enhances the classification performance on the target modality, particularly when labeled data is limited for that modality.
Key Words
breast cancer; cross-dimensional transfer learning; deep learning; mammography; medical imaging; ransfer learning
Address
R. Inbaraj: Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, India
Y.M. Mahaboob John: Department of Electronics and Communication Engineering, Mahendra College of Engineering, Salem – 636106, Tamilnadu, India
K. Murugan: Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu – 638401, India
V. Vijayalakshmi: Department of Computer Science with Artificial Intelligence, Lakshmi Bangaru Arts and Science College, Melmaruvathur – 603319, Chengalpet, Tamilnadu, India
Abstract
Early diagnosis and treatment are essential to minimizing the effects of heart based disease, which are the main causes of number death rates in and around the world. The nature of work focuses on using machine learning methods to predict cardiac problems early stage. Machine learning algorithms are implemented to analyze for large number of datasets and finding the complex patterns related to heart disease risk. Generally diseases are also many types of heart - based diseases. By integrating various data sources such as demographic information, medical history, genetic data, and lifestyle factors, machine learning models can effectively predict the likelihood of developing heart diseases. Papers represent an overview of the current research and its highlight the role of major machine learning in enhancing for early prediction capabilities. The results imply that machine learning algorithms may enhance risk classification and enable individualized therapies for the prevention and treatment of cardiac disorders. Future research and major studies are required to start the reliability and medical utility of these models in real-world healthcare settings. The attributes will be support for the finding the early stages of various heart diseases.
Key Words
data mining; echocardiogram; genetic data; machine learning; prediction; validation
Address
V. Kamakshi and S. Prasanna: Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India
