Abstract
Diabetes is a chronic illness with high morbidity and mortality that influences the quality of life considerably around the globe and thus early and correct prediction is crucial in the effective management and treatment. Nonetheless, the clinical information used is problematic because it is difficult to predict diabetes development in patients, given the complexity and variability of the data. This paper proposes a deep learning-based model with a Long Short-Term Memory (LSTM) recurrent neural network and improved preprocessing and feature selection algorithms. First, Z-score normalization is used to standardize the data, enhancing consistency and identifying abnormalities. Then, to achieve the best feature selection, the Grey Wolf Optimization (GWO) is used to improve predictive performance by identifying the most relevant clinical attributes without falling into local optima. Lastly, the LSTM-RNN model is applied to extract temporal dependencies and latent patterns in the data to correctly classify the data. Through experimentation, it has been shown that the proposed approach clearly exceeds conventional techniques based on all available measures of performance: accuracy; precision; recall; F1 score; and computational efficiency. As indicated by these results, this LSTM-RNN-GWO model shows promise as a valuable resource in the area of predictive analytics related to diabetes care, providing great benefit to patients through its use in early identification of diabetes and subsequent enhancement of their clinical experience.
Key Words
early detection; diabetes condition; GWO; LSTM-RNN; mendeley data; standard deviation; z-score normalization
Address
Wasim Raja: Department of Computer Science, Jamal Mohamed College (Autonomous), (Affiliated to Bharathidasan University), Tiruchirappalli, Tamilnadu, India
Chandraprabha K.: Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamilnadu, India
V. Ruckmani: Department of Computer Science and Applications, KMG College of Arts and Science, Vellore, Tamil Nadu, India
S. Gowri: Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts and Science for Women,
Perambalur, Tamil Nadu, India
Abstract
Leiomyosarcoma is a rare type of cancer that spreads to various parts of the body to create an aggressive form of complex tissue sarcoma disease. Artificial Intelligence (AI) powered technologies play a vital role in screening medical images to identify sarcoma types of diseases for early diagnosis and treatment to avoid cancer risks. In the preliminary stages, the machine learning and deep learning models potentially impact the identification of cancer levels; due to the invasive point of image degradation and feature inconsistency levels, the precision level attains low accuracy, leading to higher false negatives. To solve these problems, to propose an Optimal Particle Swarm Intelligence Technique (OPSIT) for feature selection with Long Short-Term Memory Gated Recurrent Neural Network (LSTM-GRNN) to identify the disease effectively. Initially, a bilateral wavelet filter (BWM) is carried out preprocessing to normalize the feature scaling and improve the image scalar margins. Then, the Linear Reiterative Clustering (LRC) algorithm is applied to segment the non-invasive point of the disease scalar region to split the cancer cells. Further, to scale, the active disease margins in cancer cell features are evaluated with OPSIT to reduce the non-relation feature in the segmented image region. Finally, the LSTM-GRNN algorithm is applied to train the scaled entity of the cancer image region, with Actual threshold margins to identify the disease region accurately. The proposed system increases the proper positive actual scaling region of the cancer region to increase precision rate and attain high accuracy, sensitivity, specificity, and ROC performance compared to the other systems.
Address
G. Petchinathan: Department of Biomedical Engineering, Sri Shanmugha College of Engineering and Technology, Pullipalayam, Morur, Sankari (Tk), Salem (Dt.)
Parveen Begam Abdul Kareem: Department of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
Shanmugaraja P.: Department of InformationTechnology, Sona College of Technology, Salem, Tamil Nadu India
Anita Venaik: Department of InformationTechnology, Amity Business School, Amity University, Noida, India
Selva Lakshmi C.B.: Department Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India
Abstract
Pharmaceutical industry works under the strict Good Manufacturing Practice (GMP) conditions which require strong systems to guarantee the quality of the product, integrity of data and adherence to regulations. Manufacturing Execution Systems (MES) have become one of such key tools in the realization of such goals. The purpose of the study is to assess the application and functioning of PAS X MES among the GMP-regulated manufacturing facilities, in particular, its operational effectiveness, compliance benefits, as well as difficulties faced. Multi-site observational analysis was used where site survey, system audit data and key stakeholder interviews were used. Pre- and post- implementation performance measures were measured quantitatively. The outcomes have shown 25 percent decrease in the batch cycle time, increasing Right First Time rates to 95 percent and a drop in process deviations by 66.7 percent. The metrics on compliance were highly improved where there was a 100 percent compliance with the 21 CFR Part 11 standard on the use of electronic signatures and the audit score has improved by 38.5 percent. Additionally, the time taken in review by QA per batch decreased by 62.5 percent making the processes of release of batches faster. Along with these improvements, issues that included spending more time with the validation in the first place and the reluctance of the users in the beginning made clear the necessity of a proper management of the change. Comparative study with other MES systems determined that the implementation speed and user satisfaction characterized PAS X with competitive advantage. This paper highlights PAS X MES as a game-changer to GMP-compliant pharmaceutical manufacturing, which can bring organizational operational agility and digital maturity to Pharma 4.0 movements.
Key Words
batch cycle time; data integrity; good manufacturing practice (GMP); manufacturing execution systems (MES); PAS X MES; Pharma 4.0; electronic batch records (EBR); pharmaceutical manufacturing
Address
Sahana Vasudev: Research and Development Department, Takeda Pharmaceuticals, Boston, Massachusetts, USA
Abstract
Data anonymization in healthcare is essential for protecting sensitive patient information while enabling secure usage for research, analytics, and AI-driven clinical decision-making. In this study, the MIMIC-III - Deep Reinforcement Learning dataset was used, which contains comprehensive electronic health records (EHRs) of ICU patients. Data preprocessing was performed using Min-Max Normalization to scale numerical features and ensure consistency. Anonymization techniques such as pseudonymization, generalization, suppression, data masking, and statistical methods like k-anonymity, l-diversity, and t-closeness were applied to safeguard patient privacy. The anonymized dataset was then utilized for predictive modelling using AI techniques including Random Forest and LSTM. Results demonstrated that privacy was maintained with 0% PII leakage, while predictive accuracy remained high, achieving accuracy of 94.6%, precision of 93.8%, recall of 92.5%, and F1-score of 93.1%. This study highlights that effective data anonymization ensures compliance with HIPAA and GDPR while retaining the utility of healthcare data for advanced analytics and AI applications.
Key Words
AI analytics; data anonymization; GDPR; healthcare; HIPAA; k-anonymity; MIMIC-III; patient privacy; pseudonymization;
Abstract
Chronic Kidney Disease (CKD) is among the most significant global health concerns, particularly in terms of its insidiousness during the first stage of its development and gradual devastation throughout the years. There is a prospect of utilizing Electronic Health Records (EHRs) to improve the outcome due to the ability to address problems at an early stage to deliver the most efficient intervention. The paper presents an intelligent predictive analytics system of healthcare in Abu Dhabi healthcare systems that is built on the EHR data collected. The pipeline of the framework is systematic and it entails data preprocessing, feature extraction and classification. The preprocessing phase is assigned to aligning the data, its coherence, and the removal of redundancies and the handling of missing values across all the EHR datasets. The step is relevant due to the heterogeneous nature of clinical information being rather complex. In summarizing the data, Principal Component Analysis (PCA) is applied to extract the features by subjecting the data to the process to compress the data and retain the most clinical information. This improves the computational and model efficiency and performance by removing noise and redundancy. It is then inputted into the constructed Long Short-Term Memory (LSTM) network due to its learning capabilities which give long-range dependencies and temporal patterns of sequential patient information. Precision, recall and F1-score, are also used to test the effectiveness of the model by determining whether the model is effective in the proper identification of CKD cases. The findings show that LSTM model is better than the traditional classifiers it is more predictive and robust. As highlighted in this paper, advanced deep learning methods might be used on EHR data to aid in the prompt identification of CKD and enhance the clinical decision-making process. The suggested framework is flexible and can be extended and provide useful information on how the framework can be applied in real-life healthcare.
Key Words
accuracy; CKD; F1-score; harmonization; HER; LSTM; precision; recall
Address
Pradeep Balaji B.: Technical Specialist Network – Security, Tech Hat Pvt Ltd, Bangalore, India
Gayathri M.: Operations Head, Willron Electronics, Bangalore, India
Deepika Sirmoria: Computer Science Engineering, Anurag University, Ghatkesar, India
Job Prasanth Kumar Chinta Kunta: Lead Solution Data Architect, The Automobile Association, London, United Kingdom
Abstract
Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specially for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models' random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients' outcomes through targeted therapy.
Key Words
breast cancer; logistic regression; machine learning; recurrence; random forest
Address
Deepa B.G.: Department of Computer Science, Christ University, Bangalore, India
Velmurugan R.: Department of Computer Science, Kristu Jayanti (Deemed to be University), Bangalore, India
Narender M., Suhaas K.P: Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India
