Volume-5 Issue-1, November 2024 – International Journal of Preventive Medicine and Health (IJPMH)

E817613050125

IJPMH Journal — Fri, 24 Jan 2025 10:08:12 +0000

The International Journal of Preventive Medicine and Health (IJPMH) has ISSN 2582-7588 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in March, May, July, September, November and January. The journal aims to publish high-quality peer–reviewed original articles in the area of Preventive Medicine and Health that covers such as the Nipah Virus, COVID-19(Corona Virus), Henipa Viruses, Antibiotic Resistance, Breastfeeding, Behavioural Health, Clinical Trials, Community Engagement, Community Health, Child/Adolescent Health, Chronic Diseases, Disease Transmission, Health Informatics, Health Care Management, Health Care Quality, Efficiency, Human Nutrition, Immunization, Maternal & Child Health, Primary Healthcare, Public Health, Reproductive Health, Vaccination, Clinical and Epidemiological Diagnosis and Management, Health care, Epidemiology, Biostatistics, Public Health Administration, Health Care Delivery, National Health Problems, Medical Anthropology, Social Medicine. #Nipah Virus #COVID-19(Corona Virus) #Henipa Viruses #Antibiotic Resistance #Breastfeeding #Behavioural Health #Clinical Trials #Community Engagement #Community Health #Child/Adolescent Health #Chronic Diseases #Disease Transmission #Health Informatics #Health Care Management #Health Care Quality, Efficiency #Human Nutrition #Immunization #Maternal & Child Health #Primary Healthcare #Public Health #Reproductive Health #Vaccination #Clinical and Epidemiological Diagnosis and Management #Health Care #Epidemiology #Biostatistics #Public Health Administration #Health Care Delivery #National Health Problems #Medical Anthropology #Social Medicine #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

AI-Driven Personalized Fitness Coaching with Body Type-Based Workout and Nutrition Plans and Real-Time Exercise Feedback
Ilukpitiya I.M.D.J.R. B¹, Herath H.M.R. B², Rajakaruna R.H.M.S.A³, Herath M.H.S.M⁴, Koliya Pulasinghe⁵, Jenny Krishara⁶

¹Ilukpitiya I.M.D.J.R. B, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

²Herath H.M.R. B, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

³Rajakaruna R.H.M.S.A, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

⁴Herath M.H.S.M, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

⁵Koliya Pulasinghe, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

⁶Jenny Krishara, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka.

On 23 May 2025, the International Journal of Preventive Medicine and Health (IJPMH) retracted the article “AI-Driven Personalized Fitness Coaching with Body Type-Based Workout and Nutrition Plans and Real-Time Exercise Feedback” from Volume-5 Issue-1, November 2024 due to discovering misconduct and conflict of interest. For more detailed information, please visit following policies:

Misconduct: https://www.ijpmh.latticescipub.com/plagiarism-policy/

Conflict of Interest: https://www.ijpmh.latticescipub.com/competing-interests/

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C72640911322

IJPMH Journal — Fri, 24 Jan 2025 10:02:15 +0000

Skin Cancer Detection Using Machine Learning
Usman Ibrahim Musa¹, Apash Roy², Musa Ibrahim Musa³, Umar Muhammad Babani⁴, Aminu Ibrahim Musa⁵

¹Usman Ibrahim Musa, Faculty of Science and Technology, Islamic Science University of Malaysia, Nilai, Malaysia.

²Apash Roy, Professor, School of Computer Applications, Lovely Professional University, Punjab, India.

³Musa Ibrahim Musa, Faculty of Computer Science and IT, Bayero University Kano, Kano, Nigeria.

⁴Umar Muhammad Babani, Department of Computer Science & Engineering, Integral University, Lucknow (U.P), India.

⁵Aminu Ibrahim Musa, Head of the Department of Information Technology, ESGT Benin University, Cotonou, Benin Republic.

Abstract: Skin-related issues often serve as indicators of underlying health problems in other parts of the human body, adversely affecting an individual’s overall fitness and well-being. However, such issues are frequently ignored, as they are perceived to be either painless or have minimal to no impact on daily life. To address this challenge, this system was developed with the aim of early detection of skin cancer. The system enables users to upload images or videos of the affected area in real-time, utilising a skin cancer detector to identify existing conditions, determine the type of cancer (if present), and provide instant feedback. The system is precisely configured to deliver highperformance results, offering real-time recommendations for medications or treatments based on its findings. By reducing the stress and inconvenience associated with dermatological consultations, this system empowers individuals to accurately identify skin-related issues and make informed decisions about seeking further medical attention from a qualified dermatologist. Powered by image processing using OpenCV, a convolutional neural network (CNN), and machine learning techniques, the system excels in accurately recognising various skin conditions. It can detect conditions such as nevus, vascular lesions, seborrheic keratosis, basal cell carcinoma, melanoma, pigmented benign keratosis, squamous cell carcinoma, dermatofibroma, and actinic keratosis, while also recommending appropriate remedial health solutions.

Keywords: Skin Cancer, Machine Learning, CNN
Scope of the Article: Health Care Management

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B105205020125

IJPMH Journal — Fri, 24 Jan 2025 09:55:48 +0000

A Novel Method to Detect Lymphangioleiomyomatosis (LAM) Using CNNLSTM on Computed Tomography
Sithika Seema. S¹, Sumathy. R²

¹Sithika Seema. S, Department of Computer Science and Engineering, Kalaignarkarunanidhi Institute of Technology Coimbatore (Tamil Nadu), India.

²Ms. Sumathy. R, Assistant Professor, Kalaignarkarunanidhi Institute of Technology Coimbatore (Tamil Nadu), India.

Abstract: “Detection of Lymphangioleiomyomatosis (LAM) using CNN-LSTM framework on CT images” Lymphangioleiomyomatosis (LAM) is a progressive lung disease marked by uncommon cystic growths, often diagnosed using high-resolution CT scan images. The complex nature of LAM’s radiological findings and the need for specialised knowledge make an early and accurate diagnosis difficult, even though it is essential for successful treatment. To automate the detection of LAM in CT images, this study introduces an advanced deep learning approach that integrates a CNN with an LSTM network. A well-annotated dataset of CT scans from patients with LAM and healthy controls was used to create and evaluate the model. To increase model robustness and generalizability, extensive preprocessing was used, including lung area segmentation and data augmentation. Accuracy, sensitivity, specificity, and AUCROC were among the key performance indicators that demonstrated the model’s ability to distinguish between LAM and non-LAM cases. The accuracy of the CNN-LSTM model for detecting Lymphangioleiomyomatosis (LAM) using CT scan images is approximately 80%. By providing a dependable, noninvasive, and scalable approach to early LAM identification, this novel CNN-LSTM design lessens the need for expert interpretation and improves diagnostic effectiveness.

Keywords: Lymphangioleiomyomatosis (LAM) disease, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep Learning, Image Classification, CT Scan Images.
Scope of the Article: Community Health

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F104604060924

IJPMH Journal — Mon, 25 Nov 2024 10:42:19 +0000

Analysing the Impact and Efficacy of Telemedicine through Data Analysis
Manoj Kumar¹, Krishna Mohan Pandey²

¹Manoj Kumar, Assistant Professor, Department of CSE, Quantum University, Roorkee, Uttarakhand, India.

²Krishna Mohan Pandey, Assistant Professor, Department of CSE, Quantum University, Roorkee, Uttarakhand, India.

Abstract: Telemedicine has become a crucial aspect of modern healthcare, utilizing digital communication technologies to offer remote medical services. This research examines the implementation, impact, and challenges of telemedicine, with a focus on data analysis to evaluate its effectiveness and future potential [1]. Through a comprehensive analysis of patient data, service utilisation, and outcomes, this study aims to highlight the strengths and areas for improvement of telemedicine. The significance of telemedicine grew notably during the COVID-19 pandemic, underscoring the need to evaluate its impact on healthcare. This paper assesses telemedicine by analysing patient outcomes, service utilization, and patient satisfaction, identifying key areas for policy and practice enhancement. Findings indicate substantial improvements in accessibility and patient satisfaction, though technological and equitable access barriers persist. The rapid evolution of telemedicine is reshaping healthcare delivery. Through data analysis, this study explores the role and potential of telemedicine in modern healthcare. Leveraging a diverse dataset, statistical and machine learning techniques are applied to uncover significant patterns and insights. Results show marked improvements in access, patient satisfaction, and cost savings for both patients and providers, while challenges such as technology adoption and digital literacy gaps remain critical. The adoption of telemedicine has accelerated significantly, transforming healthcare delivery and accessibility [2]. This study examines the impact and efficacy of telemedicine by utilising data analysis to assess its effects on patient health, access to care, cost efficiency, and patient satisfaction. Data were collected from various sources, including patient records, survey responses, and healthcare cost reports, encompassing a diverse patient population across different demographics and regions. Key metrics, including treatment adherence, recovery rates, consultation frequency, and cost reduction, were analysed. The findings reveal that telemedicine generally improves access to healthcare, particularly for rural and underserved populations, and can reduce healthcare costs by minimizing the need for in-person visits. However, challenges such as limited access to technology, variability in patient outcomes, and disparities in healthcare quality were identified. This analysis offers insights into the strengths and limitations of telemedicine, providing strategies for enhancing its efficacy and promoting equitable access. The study underscores the need for ongoing research to optimize telemedicine’s role in modern healthcare systems [3].

Keywords: Telemedicine, Telehealth, E-health, Digital Health, Remote Healthcare, Patient Outcomes.
Scope of the Article: Health Care Management

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D364814040924

IJPMH Journal — Mon, 25 Nov 2024 10:37:18 +0000

Surgical Data Science and Associated Techniques Facilitate the Development of Contemporary Equipment like Apple’s Vision Pro
Vinothkumar Kolluru¹, Sudeep Mungara², Advaitha Naidu Chintakunta³, Charan Sundar Telaganeni⁴, Lokesh Kolluru⁵

¹Vinothkumar Kolluru, Department of Data Science, Stevens Institute of Technology, Seattle, WA, (United States of America) USA.

²Sudeep Mungara, Stevens Institute of Technology, Philadelphia, PA, (United States of America) USA.

³Advaitha Naidu Chintakunta, Department of Computer Science, University of North Carolina at Charlotte, Seattle, WA, (United States of America) USA.

⁴Charan Sundar Telaganeni, Department of Computer Science, Stevens Institute of Technology, Texas, TX, (United States of America) USA.

⁵Lokesh Kolluru, Department of Civil Engineering, Syracuse University, Charlotte, (United States of America) USA.

Abstract: Artificial Intelligence (AI) has revolutionized modern surgery by enhancing every stage of patient care, from preoperative planning to postoperative monitoring. This paper examines the impact of AI in conjunction with other technologies on surgical procedures, highlighting their empirical basis and integration into clinical practice. AI’s role in facilitating personalised treatment planning through a comprehensive analysis of patient data and imaging studies, utilising techniques such as natural language processing (NLP) to extract critical insights, reassures us of its positive impact on patient care. Realtime decision support systems powered by AI improve surgical precision, enabling surgeons to navigate complex procedures with enhanced accuracy and efficiency. Furthermore, AI-driven surgical robotics exemplifies the precision achievable with these technologies, enabling minimally invasive procedures that minimise patient trauma and expedite recovery. Integrating AI with computer vision further enhances surgical capabilities by allowing machines to interpret visual data autonomously, like human perception. Convolutional Neural Networks (CNNs) play a pivotal role in image recognition and analysis, supporting tasks ranging from identifying anatomical landmarks to planning surgical procedures. Augmented Reality (AR), when combined with AI, enriches surgical practice by overlaying digital information onto real-world views, aiding in intraoperative guidance and educational training. Devices like Apple’s Vision Pro (AVP) headset showcase the potential of mixed reality technologies in enhancing surgical precision. AVP’s integration of spatial computing and AI algorithms enables real-time data analysis and decision support, ultimately transforming surgical education and procedural outcomes. Despite the transformative potential, challenges, including ethical considerations, data privacy, and regulatory frameworks, must be addressed to ensure the responsible deployment of AI in surgical settings. These challenges include mitigating biases in AI algorithms and ensuring equitable access to advanced technologies across diverse surgical specialities. The dynamic nature of AI in surgery necessitates ongoing research and development to refine AI applications, optimise surgical workflows, and enhance patient outcomes globally. In combination with contemporary technologies, AI represents a paradigm shift in surgical practice, offering unprecedented opportunities to improve patient care through personalized, precise, and efficient interventions. AI’s ongoing evolution and integration in surgery promise to reshape the future of healthcare, advancing clinical practice and medical education toward safer, more effective, and inclusive healthcare delivery systems.

Keywords: Surgical Data Science, Artificial Intelligence, Apple Vision Pro Headset, Mixed reality, Augmented reality, Virtual reality.
Scope of the Article: Health Care Management

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Volume-5 Issue-1, November 2024 – International Journal of Preventive Medicine and Health (IJPMH)

E817613050125

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C72640911322

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B105205020125

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F104604060924

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D364814040924

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