Volume-6 Issue-1, November 2025 – International Journal of Preventive Medicine and Health (IJPMH)

F112605060925

IJPMH Journal — Mon, 03 Nov 2025 07:20:42 +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

Problems in the Early Detection of Cancer
Rajabova Maftuna Rustamovna¹, Abdurashidova Kamola Turgunbayevna², Yusupov Bakhodir Karamatovich³

¹Rajabova Maftuna Rustamovna, Student, Department of Computer Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan.

²Abdurashidova Kamola Turgunbayevna, Associate Professor, Head of the Department of Training of Scientific and Pedagogical Personnel, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan.

³Yusupov Bakhodir Karamatovich, Professor, Head of the Information Technologies and Artificial Intelligence department Military Institute of Information and Communication Technologies and Signals, Tashkent, Uzbekistan.

Abstract: This article examines the analysis of existing challenges in early-stage cancer detection and approaches for their resolution. Additionally, the article discusses the probability of metastasis in early cancer detection based on actual tumour volume, as determined through diagnostic examination results. Numerous studies have demonstrated that early-stage detection and timely treatment of cancer significantly improve patients’ survival outcomes. However, long-term survival indicators may not always correlate with treatment efficacy. This phenomenon is observed in certain patients as a result of slow disease progression or early diagnosis. While survival statistics appear to improve, actual mortality rates may remain unchanged. Despite decades of scientific research, only select early detection tests demonstrate proven efficacy in reducing cancer-related mortality rates. Nevertheless, such outcomes are sometimes achieved at the cost of detecting clinically insignificant tumour states in patient populations. Detection of cancer before metastasis and surgical resection of the tumour provides the opportunity for a complete cure. If the disease has metastasized, a combination of surgical intervention and systemic therapy (chemotherapy or immunotherapy) is employed; however, a complete cure may not always be achievable. Treatment efficacy is often dependent on the extent of metastatic disease burden. Therefore, cancer detection before the emergence of clinical manifestations and before the onset of metastasis represents the optimal therapeutic approach. In some instances, detection of metastasis at relatively early stages through screening may also enhance the efficacy of systemic therapy. If novel diagnostic tests cannot differentiate between biologically progressive and clinically insignificant tumours, early detection may lead to increased incidence rates without reducing cancer mortality. Detection of small tumours requires screening at short intervals; however, this increases the number of overdiagnoses, unnecessary tests, and false-positive results. In the context of biological heterogeneity in tumour growth rates, adapting the screening frequency to tumour kinetics plays a crucial role in balancing the benefit-to-harm ratio.

Keywords: Cancer, Metastasis, Screening, Liquid Biopsy, Medical Imaging, Artificial Intelligence (AI), The Tumour Volume Doubling Time (TVDT), Convolutional Neural Network (CNN), Deep Learning, Machine Learning.
Scope of the Article: Health Care Management

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F112205060925

IJPMH Journal — Mon, 03 Nov 2025 07:13:03 +0000

Assessment of Radon Concentration, Annual Effective Dose, and Excess Lifetime Cancer Risk in Homes Constructed with Limestone, Fired Clay Bricks, and Concrete Blocks in Al-Muthanna Province
Faiq Dakhel Saadoun

Faiq Dakhel Saadoun, Researcher, Department of Physics, Muthanna University, Al-Muthanna, Iraq.

Abstract: This study measured radon gas concentrations in three types of houses in Al-Muthanna Governorate, located in southwestern Iraq, which were built using the most common building materials in the governorate: concrete blocks, fired clay bricks, and limestone. Houses were randomly selected (20) samples for each house type to ensure comprehensive representation and determine the effect of building material type on indoor radon concentration, which helps in assessing the health risks associated with long-term exposure to this gas. It also contributes to guiding future policies towards the use of safer building materials to enhance indoor air quality and population safety. In this study, an Airthings Radon Portable Detector was used to measure radon concentrations over three consecutive days. The device was placed in living rooms at a height ranging from 60 to 100 cm above the ground, ensuring that windows and doors were closed to prevent air drafts and to provide accurate measurements. The results showed that the average radon concentration was highest in houses built of limestone (24 Bq/m³), followed by houses constructed of concrete blocks (21 Bq/m³), and lowest in houses built of fired clay bricks (16.25 Bq/m³). In terms of the estimated annual effective dose, it was higher in limestone houses (0.605 𝒎𝑺𝒗.𝒚 −𝟏 ), compared to concrete block houses (0.530 𝒎𝑺𝒗.𝒚 −𝟏 ) and fired clay brick houses (0.410 𝒎𝑺𝒗.𝒚 −𝟏 ). and The ELCR resulting from radon exposure was found to be higher in limestone houses (0.238%) and lower in fired clay brick houses (0.161%), suggesting that fired clay brick may be the most suitable choice in terms of radiation safety. Despite this disparity, all values remained within the safe limits and percentages recommended by the WHO and UNSCEAR. It can be concluded that the measured radon concentrations are within acceptable limits, indicating no radioactive hazard. The study reveals that the type of building material affects indoor radon concentrations, and the geological nature of limestone likely contributes to higher emissions. Accordingly, the study recommends using low-radonemitting building materials and periodically monitoring gas levels to maintain indoor air quality and reduce long-term health risks.

Keywords: Radon Concentration, Annual Effective Dose (AED), Excess Lifetime Cancer Risk (ELCR), Building Materials, AlMuthanna Province.
Scope of the Article: Health Care Management

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F111905060925

IJPMH Journal — Mon, 03 Nov 2025 07:06:29 +0000

Cognitive Development During Childhood: Essential in Leading to Effective Growth and Development
Radhika Kapur

Dr. Radhika Kapur, Pedagogy and Organizational Culture in Nursery Schools, Delhi University, New Delhi, India.

Abstract: The primary objective of this research paper is to provide information on the meaning and significance of cognitive development. This is essential for leading to the effective growth and development of individuals. The individuals, both within and outside the homes, are required to be well-informed about various factors that facilitate carrying out this task in an adequate manner. Family members are regarded as the primary individuals from whom learning, growth, and development occur. On the other hand, when individuals enrol in educational institutions at all levels, they generate information in terms of techniques that facilitate the desired outcomes. Therefore, cognitive development in individuals effectively takes place. One of the crucial aspects that needs to be considered is the need to upgrade listening skills. These are the skills that facilitate augmenting information and understanding across various factors. Furthermore, when data is acquired, it must be implemented effectively. Therefore, it is understood on a comprehensive basis that cognitive development is regarded as vital in leading to the upgradation of overall personality traits and standards of living for individuals. The primary concepts examined in this research paper are understanding the meaning and significance of cognitive development during childhood, measures to be implemented for promoting cognitive development, and the benefits of fostering cognitive development during this stage of life.

Keywords: Abilities, Childhood, Cognitive Development, Effective, Information, Skills, Traits, Up-Gradation.
Scope of the Article: Public Health

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F111805060925

IJPMH Journal — Mon, 03 Nov 2025 06:59:13 +0000

Augmenting Knowledge and Awareness: Vital in Leading to Up-Gradation of Standards of Living
Radhika Kapur

Dr. Radhika Kapur, Pedagogy and Organizational Culture in Nursery Schools, Delhi University, New Delhi, India.

Abstract: The primary objective of this research paper is to understand that throughout the lives of individuals in both personal and professional spheres, they need to continually augment their knowledge and awareness in various subjects and concepts. This needs to be possessed in terms of job duties and methodologies that are implemented. The different types of job duties are carried out within and outside the home. In addition, individuals need to be well-informed about the ways that facilitate carrying out all kinds of job duties and responsibilities in a well-organised manner. Furthermore, individuals must inculcate the traits of morality, ethics, diligence and conscientiousness. These are essential to carrying out job duties and responsibilities as expected by individuals in leadership positions and family members. In their personal lives, as individuals pursue educational programs and implement job duties in various types of employment settings, they must continually enhance their knowledge and awareness. As a consequence, they will render a significant contribution to doing well in one’s job duties and achieving desired goals and objectives. Therefore, it is well understood that augmenting knowledge and awareness is vital in leading to the upgradation of standards of living. The main concepts considered in this research paper are measures to be implemented in augmenting knowledge and awareness. Augmenting knowledge and understanding is vital for the progression of individuals and the benefits that result.

Keywords: Augmenting, Awareness, Effective, Information, Job Duties, Methodologies, Knowledge, Progression.
Scope of the Article: Public Health

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E110305050725

IJPMH Journal — Mon, 03 Nov 2025 06:45:11 +0000

A Framework for the Prediction of Eye Diseases using Deep Learning Models
Madhab Paul Choudhury¹, Jagannibas Paul Choudhury²

¹Madhab Paul Choudhury, Research Scholar, Department of Computer Science & Engineering, IIT ISM Dhanbad, Jharkhand, Kolkata (West Bengal), India.

²Dr. Jagannibas Paul Choudhury, Retired Professor (IT), Kalyani Government Engineering College, Kalyani, West Bengal, Professor, Department of Computer Science & Engineering, Narula Institute of Technology, Kolkata (West Bengal), India.

Abstract: Artificial Intelligence (AI) has significantly enhanced various aspects of our lives, providing practical solutions to complex problems and narrowing the gap between real-world challenges and business needs. Within the realm of AI, innovative technologies such as machine learning and deep learning have revolutionised the way we analyse data, make informed decisions, and respond to complex situations, all with the goal of understanding and resolving them efficiently. As data generation and storage continue to expand rapidly, these technologies have become essential in managing data analytics, storage systems, and decision-making frameworks. Through digital transformation, industries are being reshaped, and global services—especially in fields such as healthcare—are being enhanced to improve the quality of human life. The eye is considered the most important sensory organ for humans, playing a vital role in their overall ability to interact with the world. Unfortunately, many individuals in both rural and urban areas suffer from eye conditions such as cataracts, glaucoma, ocular hypertension, bulgy vision, etc., that affect their vision. There are various causes, including age, diabetes, and genetic and hereditary factors. Modern lifestyles, which have led to increased use of displays for digital devices, are also a factor affecting vision. This study aims to develop a predictive model for vision-related diseases by leveraging medical data and analyzing the influence of various contributing factors using deep learning methodologies. Early detection of eye conditions is crucial to prevent serious complications, as timely diagnosis plays a vital role in ensuring effective treatment. Conducting thorough assessments to identify potential indicators of eye diseases is highly encouraged. Deep learning and artificial intelligence (AI) significantly enhance the ability to detect these illnesses at an early stage. The purpose of this research is to lay the foundation for discovering a robust and adaptable solution. Specifically, the study focuses on reviewing the classification of eye diseases using advanced deep learning models, such as Custom CNN (Convolutional Neural Network), VGG16, ResNet, InceptionV3, GoogleNet, EfficientNet B2, MobileNet, and DenseNet 121. The proposed classification framework follows a series of steps: initially, it involves gathering widely accessible datasets related to eye diseases and performing data preprocessing to ensure consistent experimental conditions. The primary objective is to train the model to accurately identify symptoms of eye diseases, rather than optimizing for a narrow subset of data. Given the success of deep learning in image classification and object recognition tasks, the research emphasizes these modern techniques over conventional handcrafted approaches.

Keywords: Custom CNN (Convolution Neural Network), VGG16, ResNet, InceptionV3, GoogleNet, Efficient Net B2, MobileNet, DenseNet 121.
Scope of the Article: Health Care Management

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D108905040525

IJPMH Journal — Mon, 03 Nov 2025 06:35:04 +0000

A Machine Learning Approach for Early Detection of Breast Cancer: Performance Evaluation and Analysis
Tanusree Saha¹, Sarmistha Santra²

¹Tanusree Saha, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.

²Sarmistha Santra, Student, Department of Computer Applications, JIS College of Engineering, Kalyani (West Bengal), India.

Abstract: Breast cancer is one of the most prevalent cancers affecting women globally and continues to be a leading cause of cancer-related deaths. Early and accurate diagnosis significantly improves survival rates, but conventional diagnostic techniques are often time-consuming, costly, and prone to subjective interpretation. To address these challenges, this study focuses on developing an efficient breast cancer detection system using machine learning (ML) and deep learning (DL) algorithms. By leveraging Convolutional Neural Networks (CNNs) and several traditional ML models, the system aims to classify breast cancer efficiently based on imaging data. Utilising the Wisconsin Breast Cancer Dataset (WBCD), the project evaluates the performance of various machine learning models, including SVM, KNN, Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, AdaBoost, and XGBoost, based on accuracy, precision, and robustness. The outcomes indicate that machine learning can significantly enhance early detection efforts, providing clinicians with reliable decision-support tools.

Keywords: Breast Cancer, Machine Learning, Deep Learning, CNN, SVM, KNN, Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, AdaBoost, Boost
Scope of the Article: Breastfeeding

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Volume-6 Issue-1, November 2025 – International Journal of Preventive Medicine and Health (IJPMH)

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