Articles

An Advanced Machine Learning Approach for Enhanced Diabetes Prediction

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 Diabetes is a chronic health condition affecting millions globally, causing severe complications and burdening healthcare systems. Current machine learning methods for diabetes prediction face challenges such as data imbalance, limited generalizability, and computational inefficiency. This study proposes a novel method that combines K-Nearest Neighbors (KNN), clustering techniques, Synthetic Minority Over- sampling Technique (SMOTE), and Random Forest for outcome classification to address these issues. The PIMA Indian Diabetes Dataset was used to evaluate the approach, achieving accuracy of 87.50%. However, the study has limitations, such as dependency on specific datasets and computational complexity. Future work will focus on validating the method across diverse datasets, optimizing computational efficiency, and developing real-time prediction capabilities.

Development of Marketing Strategies with Digital Enhancements to Increase Patients’ Buying Decision in a Fertility Center

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Infertility is a significant global concern, affecting a substantial portion of the adult population. Defined as the inability of a married couple to achieve pregnancy after 12 months of regular unprotected intercourse, its prevalence is increasing worldwide. According to the World Health Organization’s 2023 data, one in six adults experiences infertility, with notable variations across income brackets. Indonesia also faces this issue, affecting 10–15% of reproductive-age couples. This research examines the critical role of marketing strategies, specifically the 7Ps marketing mix (Product, Price, Place, Promotion, People, Physical Evidence, and Process), in influencing consumer buying decisions in fertility centers. The study aims to identify key factors that significantly impact patient choices and propose strategic improvements to enhance patient acquisition and retention. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), this research evaluates the relationship between marketing mix components and patient decisions. The findings highlight the importance of competitive pricing, effective promotion, high-quality staff, and efficient service processes in attracting patients. Additionally, it underscores the need for facility improvements, continuous service quality enhancement, and ensuring access to fertility centers. Furthermore, the role of digitalization in enhancing marketing strategies is critical. Incorporating digital tools and AI can significantly improve patient engagement, streamline administrative processes, and provide personalized care. By leveraging digital platforms for promotions, virtual consultations, and patient follow-ups, fertility centers can enhance their reach and service efficiency. This integration of digital solutions is crucial for staying competitive in a rapidly evolving healthcare market. This study provides actionable insights for fertility centers to refine their marketing strategies, including digitalization to improve patient acquisition and retention.

Factors Affecting Medication Adherence among Elderly People with Chronic Illness in Surigao City

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A descriptive quantitative study in Surigao City, Philippines, investigated the factors affecting medication adherence in elderly individuals with chronic illnesses. The study involved 50 geriatric respondents to whom the researcher-made questionnaires adapted from the Morisky Medication Adherence Scale (MMAS-8) were administered. Most respondents exhibited a predominantly very high adherence level to their medication regimen, indicating overall satisfactory adherence. Various demographic factors such as age, gender, education, occupation, marital status, income, and clinical diagnosis did not significantly associate with adherence. Exploratory factor analysis identified four key factors affecting medication adherence namely: effectiveness of the medicine, desire to be treated, physician’s good prescription, and influence of positive observations. The study recommends that healthcare providers should implement a continuous monitoring and evaluation process, facilitating adjustments and refinements based on evolving circumstances among the elderly population. Healthcare systems and policymakers should explore strategies to make medications more affordable and accessible, particularly for those with limited financial resources. Future research should delve deeper into each factor to gain a comprehensive understanding of medication adherence.

Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare

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This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

 

A Study on Secure Wireless Mobile Data Exchange System in Healthcare Using RFID (Radio-Frequency Identification)

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IoT provides a unified framework for coordinating the use of diverse real-world and digitally- enhanced healthcare assets. Telemedicine is appealing in poor countries because of a lack of access to healthcare services, an aging population with chronic diseases, rising healthcare expenditures, and a need to monitor patients remotely. The Internet of Things can provide individualized health services that enhance people’s quality of life while also reducing the burden on public health infrastructure. Therefore, the purpose of this work is to catalog, compare, and categorize previous research into Healthcare IoT (HIoT) systems. In order to better understand the backdrop, we looked at research articles on RFID-based Secure Wireless mobile data exchange systems in healthcare. There was also discussion of other crucial issues including security and interoperability. At the conclusion of this report, we briefly discuss the main advantages and disadvantages of each study. Lastly, there is a dearth of research into the security and interoperability concerns of IoT design in healthcare. Important outcomes of IoT in healthcare include improved information sharing, shorter hospital stays, and lower overall healthcare expenses. IoT in healthcare faced major obstacles due to concerns over patient privacy and data security.

A Brief Review of Healthcare System Transformation Directions

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The health care systems globally are undergoing constant changes and redesign processes within the framework of increasing patient expectations and increasingly challenging objectives related to the health of populations, cost reduction and improvement of health care quality. New approaches of healthcare reimbursement and payment are being implemented with the aim to contain costs. In parallel, there are relentless efforts to improve the quality of care through the reorganization of the healthcare team, increasingly relying on evidence, putting the patients at the centre of care, and greater participation of the patients in the process of healthcare. Overcoming the fragmentation of health systems is a major challenge for most countries of the world.  In this light, healthcare system innovation and transformation, in order to meet the expectations of the patients, healthcare professionals and other stakeholders involved is of critical importance. System thinking offers an opportunity for healthcare professionals and experts of health system administration, management and strategic planning to fine-tune the health system so that it can fulfil its aspirations.

Knowledge-Based Performance Management Framework for Small Public Health Facility: A Case Study of Clinic T in City B, Indonesia

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The healthcare service system in Indonesia is divided into two levels, first-level healthcare services, and advanced-level healthcare services. One of the first-level healthcare services is the small public health facility or clinics. The healthcare system in Indonesia requires patients to seek treatment at a first-level health care system first and prohibits seeking treatment at an advanced-level healthcare system unless emergency or necessary. However, research on the performance management system for clinics is still very minimal. This research is intended to design a performance management framework using Clinic T in City B, Indonesia, as a case study. The flow of research methodology in this study is started with problem identification, continued with framework selection analysis. The selected framework in this study is the Knowledge-Based Performance Management System (KBPMS). Performance framework for Clinic T and the performance indicators are presented, along with the linkage between performance variables and one of the simple ways to show the clinic’s performance for easier evaluation. The proposed framework is expected to be suitable for other clinics in Indonesia and can be used as a foundation for other clinics in designing their own performance management framework.