Volume-5 Issue-5, July 2025 – International Journal of Preventive Medicine and Health (IJPMH)

D108505040525

IJPMH Journal — Mon, 07 Jul 2025 11:03:49 +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

Next-Gen Surgery Assistance with IoT: Real-Time Health Monitoring of ECG, Cardiac Activity, and Neural Responses
B. Vijayendra Reddy¹, Sandeep Kulkarni², Vivek More³, Mangesh Patil⁴, Gaurav Punyani⁵

¹B. Vijayendra Reddy, Department of Computer Science and Engineering, Sandip University, Nashik (Maharashtra), India.

²Dr. Sandeep Kulkarni, Department of Computer Science and Engineering, Ajeenkya Dy Patil University, Ulc-Building, Pune (Maharashtra), India.

³Vivek More, Department of Computer Science and Engineering, Ajeenkya Dy Patil University, Ulc-Building, Pune (Maharashtra), India.

⁴Mangesh Patil, Department of Master of Computer Applications, Ajeenkya Dy Patil University, Ulc-Building, Pune (Maharashtra), India.

⁵Gaurav Punyani, Department of Master of Computer Applications, Ajeenkya Dy Patil University, Ulc-Building, Pune (Maharashtra), India.

Abstract: The increasing demand for advanced healthcare solutions has led to the integration of IoT in medical systems. This paper introduces a novel IoT-based health monitoring and surgery assistance system that combines ECG signal monitoring, heartbeat tracking, and nervous system analysis to assess overall health conditions. The system leverages wearable medical devices and an IoT architecture to provide real-time data, enabling remote patient monitoring and assisting during surgeries. Key features include online health condition display accessible from anywhere and innovative device integration for seamless tracking. Experimental results demonstrate the system’s high accuracy and real-world applicability, offering a transformative approach to modern healthcare.

Keywords: IoT healthcare, ECG monitoring, heartbeat analysis, nervous system monitoring, surgery assistance, wearable medical kit, real-time health monitoring.
Scope of the Article: Maternal Health

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D107905040525

IJPMH Journal — Mon, 07 Jul 2025 10:55:25 +0000

Hybrid AI: Bridging the Gap Between AI Innovation and Precision Medicine
Vinit Rajiv Yedatkar¹, Sudarshan Baswantrao Gopchade², Krushna S. Jagtap³

¹Vinit Rajiv Yedatkar, Pharm-D, Aditya Pharmacy College, Beed; Affiliated under Dr. Babasaheb Ambedkar Technological University Lonere, Raigad (Maharashtra), India.

²Sudarshan Baswantrao Gopchade, Pharm-D, Aditya Pharmacy College, Beed; Affiliated under Dr. Babasaheb Ambedkar Technological University Lonere, Raigad (Maharashtra), India.

³Krushna S. Jagtap, Student, Pharm-D, Dr. Babasaheb Ambedkar Technological University Lonere, Raigad (Maharashtra), Raigad, India.

Abstract: Accelerated development in artificial intelligence (AI). The phrase has encouraged advancements in drug discovery and development. In this study, we probe the constraints of AI models—AlphaFold, AtomNet, and Insilico GANs—on predictive precision and cross-therapeutic generalizability. We propose HybridAI, a hybrid AI framework that combines geometric deep learning (GDL), reinforcement learning (RL), and federated learning (FL) for improved predictive modelling of drug-target interactions. They were evaluated against metrics such as ROCAUC, RMSD, and hit-rate accuracy across four therapeutic categories: oncology, antimicrobial resistance, neurodegenerative disease, and autoimmune disease. HybridAI was implemented and validated on a dataset of 150 structurally diverse compounds from ChEMBL and DrugBank. The model outperformed current AI frameworks, achieving 92% accuracy in predicting drug-kinase interactions, with a 34% reduction in toxicity prediction error compared to conventional ADME models. A case study involving non-small cell lung cancer (NSCLC) illustrated the in vitro applicability of HybridAI. The system correctly identified afatinib as a potent kinase inhibitor, with a predicted binding affinity of 89%. The prediction was confirmed by molecular docking and in vitro assays within 14 days. Our findings highlight the limitations of single-purpose AI models and underscore the need for hybrid systems, such as Hybrid AI, to enhance precision, flexibility, and scalability. The research supports the use of advanced learning methodologies to facilitate personalised medicine and expedite the drug development process. By integrating various AI methods, HybridAI raises the bar for intelligent drug discovery architectures. The rapid growth of artificial intelligence (AI) in drug discovery necessitates a critical evaluation of its predictive validity and therapeutic applicability. The current study aims to compare the predictive performance of different AI-based models for predicting the success of drug therapy and to introduce a novel combinational AI method, HybridAI, to enhance predictive strength and cross-therapeutic applicability. Seven AI models, such as AlphaFold [1], AtomNet [2], and Insilico GANs [3], were thoroughly assessed for drug efficacy, toxicity, and binding affinity prediction in four disease areas: oncology, antimicrobial resistance, neurodegenerative disorders, and autoimmune diseases. Normalized metrics such as receiver operating characteristic (ROC-AUC), root mean square deviation (RMSD), and hit-rate accuracy were used to evaluate the models. HybridAI, a new combinational model incorporating geometric deep learning GDL [4], reinforcement learning RL [5], and federated learning FL [6], was tested on a 150-structurally different compound dataset that was extracted from ChEMBL [7] and DrugBank [8]. Comparative analysis revealed that the existing AI models are 78– 85% accurate in target-specific drug design but show extreme variability (12–28%) in cross-therapeutic generalizability. Hybrid AI outdid individual models by achieving 92% drug-kinase interactions (compared to 79% with AlphaFold¹) and a 34% reduction in errors in toxicity prediction versus conventional ADMET predictors. HybridAI was cross-validated by a case study by repurposing kinase inhibitors on non-small cell lung cancer (NSCLC), with correct prediction of afatinib¹⁰ through 89% binding affinity, and subsequently confirmed in vitro within 14 days. The findings highlight the limitations of single AI models for drug discovery and underscore the importance of hybrid AI architectures in delivering greater predictive reliability. By utilising multi-modal learning frameworks, HybridAI provides an open and adaptable infrastructure that facilitates the acceleration of precision medicine, reduces inefficiencies in drug development, and personalises therapeutic strategies.

Keywords: Artificial Intelligence, Drug Discovery, HybridAI, Precision Medicine, Deep Learning.
Scope of the Article: Medicine

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D107805040525

IJPMH Journal — Mon, 07 Jul 2025 10:45:43 +0000

Advancing Diagnostic Accuracy in Lung Disease Severity Classification Using Multi-Domain Features
Urvashi Deshmukh¹, Prapti Deshmukh²

¹Urvashi Deshmukh, Scholar, Department of Computer Science, Dr. G. Y. Pathrikar College of CS and IT, MGM University, Aurangabad (Maharashtra), India.

²Prapti Deshmukh, Department of Computer Science, Dr. G. Y. Pathrikar College of CS and IT, MGM University, Aurangabad (Maharashtra), India.

Abstract: Accurate classification of lung diseases is crucial for early diagnosis and effective treatment. This study presents an optimised classification framework that utilises multi-domain feature extraction and a deep neural network (DNN) for categorising lung disease severity from CT scan images. The dataset collected from a local hospital includes 266 CT scans of Lung cancer, COVID-19, and pneumonia, categorized into mild (43 images), moderate (82 photos), and severe (141 images) cases. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, ensuring equal representation across categories. A total of 30 multi-domain features were extracted using a comprehensive feature extraction methodology that combined wavelet packet decomposition (WPD) with statistical, texture, shape, edge detection and Grey Level Cooccurrence Matrix (GLCM) features. These features captured diverse spatial and frequency-based characteristics of lung disease patterns, enabling robust model input. This study focuses on classification based on the severity of patient condition within three different classes: Mild, Moderate, and Severe, related to lung disease. The classification was performed using a Deep Neural Network (DNN) with fine-tuned hyperparameters. The model achieved a training accuracy of 95%. The findings underline the potential of this approach in improving automated diagnostic systems. The extracted features provide a comprehensive representation of disease patterns, while the DNN leverages these features for precise classification. This methodology offers valuable insights for applications in medical imaging. This research contributes to the field of medical image analysis by integrating robust feature extraction techniques with advanced classification models, paving the way for more accurate and reliable lung disease diagnosis.

Keywords: Synthetic Minority Oversampling Technique, Wavelet Packet Decomposition, Grey Level Co-occurrence Matrix, Deep Neural Network.
Scope of the Article: Community Health

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D107405040525

IJPMH Journal — Mon, 07 Jul 2025 10:36:40 +0000

Awareness and Perception of Green Hospital Concept Among the Healthcare Professionals in Jalandhar
Ebenezer Osei Bonsu¹, Obed Appiah², Astitva Awasthi³, Priya Kushwaha⁴

¹Ebenezer Osei Bonsu, Student, Department of Mittal School of Business, Lovely Professional University, Jalandhar (Punjab), India.

²Obed Appiah, Student, Department of Mittal School of Business, Lovely Professional University, Jalandhar (Punjab), India.

³Astitva Awasthi, Student, Department of Mittal School of Business, Lovely Professional University, Jalandhar (Punjab), India.

⁴Dr. Priya Kushwaha, Assistant Professor, Department of Mittal School of Business, Lovely Professional University, Jalandhar (Punjab), India.

Abstract: The integration of sustainable practices with healthcare improvements takes place through hospitals focusing on decreasing the environmental impacts of healthcare facilities. Healthcare professionals often fail to effectively implement green hospital practices due to a lack of awareness about their potential benefits. An investigation has been conducted to examine how Jalandhar healthcare professionals perceive and implement green hospital practices, as well as their awareness levels and the challenges they encounter in practice. The research demonstrates why healthcare workers in Jalandhar require standardised education, together with institutional backing that supports Sustainable Development Goal 13 (Climate Action). Healthcare institutions generate substantial environmental contamination due to their excessive use of energy resources and the production of large amounts of waste. The adoption of sustainable practices in green hospitals leads to better healthcare outcomes, along with stronger environmental resilience, as they focus on energyefficient designs, sustainable building standards, and proper waste management. Green hospitals are becoming increasingly common worldwide, yet their adoption remains relatively low in India. The present efforts primarily focus on waste management solutions and energy conservation practices, but sustainable practices require further development. Healthcare professionals throughout Jalandhar received an assessment through a structured questionnaire regarding their knowledge levels and perceptions of green hospital installation, as well as the implementation barriers they face. The survey inquiry collected information about demographics, as well as participants’ knowledge levels of environmental hospital practices and their perceived advantages, and their organisations’ support deficiencies. Medical staff from various hospitals, along with nursing personnel and administrative personnel, participated in the survey. Most healthcare workers demonstrate knowledge about green hospital practices; however, they often struggle with insufficient support and training from their institutions. Key results include: Among the surveyed medical staff, only 65 per cent demonstrated awareness about energy conservation, yet 40 per cent. The per cent grasped complete green hospital systems. Seven out of ten healthcare providers (75%) showed they needed training on sustainability practices, yet they had not received any formal instruction. The main obstacles cited by hospital personnel included [1] 55% feeling organizations lacked support, [2] 48% reporting financial limitations, and [3] 42% experiencing resource scarcity. The complete realization of green hospital benefits requires stakeholders to establish training programs as their top priority. The training of green hospitals must become an obligatory component in healthcare education programs through government policies. Sustainable healthcare practices need institutional support, which enables the provision of essential funding and performance-based policies [4]. Numerous international healthcare facilities have proven how green hospitals cut costs and create better patient results [5]. Green infrastructure implementation in hospitals of developed nations generates both 30% less energy expenses and better patient experiences [6]. India needs to develop customized green hospital strategies to gain similar advantages as other nations have achieved. Healthcare institutions should integrate the green hospital concept into their accreditation standards, together with performance evaluation procedures. The advancement of sustainable healthcare practices depends heavily on three essential elements: improved awareness, organized training programs, and company-wide support and dedication. The research will support future investigations into practical strategies for implementing green hospital practices throughout India.

Keywords: Green Hospitals, Environmental Sustainability, Healthcare Professionals, Awareness, Perception, India.
Scope of the Article: Health Care Management

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D106805040525

IJPMH Journal — Mon, 07 Jul 2025 10:26:54 +0000

Survival Model of Cervical Cancer Patients using the 3-Parameter Weibull Distribution Model
Hassan Swedy Lunku¹, Ismail Juma Kaudunde², Kidney Chillingo³

¹Hassan Swedy Lunku, Assistant Lecturer, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.

²Ismail Juma Kaudunde, Lecturer, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.

³Kidney Chillingo, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.

Abstract: This study aimed to evaluate a parametric survival model of cervical cancer patients treated with ORCI, and a case study was considered for a description of the model. The survey of survival times of cervical cancer patients may help reduce cervical cancer outcomes. Data on socio-demographic characteristics, reproductive status, stages, treatment, and follow-up of the treatment, abstracted from medical files, were considered in model development. The primary objective of this study was to analyse cervical cancer survival times from the diagnosis period using a three-parameter Weibull distribution model. The analysis was performed using the open-source statistical software R and Minitab. The three-parameter Weibull distribution is very flexible for random data fitting; moreover, it has a strong adaptability for different types of probability distributions. When the three parameters are well chosen, it can be equal to or approximate some other statistical distribution. However, the three parameters were estimated to utilize the Weibull model successfully. The distribution of survival times of cervical cancer patients, as analysed, follows the three-parameter Weibull distribution, with required test statistics including the Anderson-Darling significant value and standard probability plots. The use of other parametric distribution models, such as the Gamma, three-parameter Gamma, and Weibull distributions, which encompass various types of hazard functions, is recommended for future studies.

Keywords: 3-Parameter Weibull Distribution Model, Parametric Model, Survival Times, Cervical Cancer.
Scope of the Article: Community Health

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Volume-5 Issue-5, July 2025 – International Journal of Preventive Medicine and Health (IJPMH)

D108505040525

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