Volume-5 Issue-2, January 2025 – International Journal of Preventive Medicine and Health (IJPMH)

B105105020125

IJPMH Journal — Fri, 24 Jan 2025 09:40:58 +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

A Survey on Liver Cancer Detection Using Hyperfusion of CNN and SVM in Machine Learning
Sasikala R¹, Kalaiselvi N²

¹Sasikala R, Assistant Professor, Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

²Kalaiselvi N, Student, Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Since liver cancer ranks among the most aggressive renditions of the disease, improving patient outcomes requires early identification. We propose an inventive tactic to liver cancer detection by integrating CNN and SVM. CNNs, known for their powerful feature extraction capabilities, are particularly effective in analysing complex medical images. SVMs, on the other hand, are efficient classifiers that can separate data points in highdimensional spaces with accuracy. By combining the feature extraction capabilities of CNN with the classification efficiency of SVM, the proposed model aims to enhance the accuracy and robustness of liver cancer detection. The experimental results reveal that the fused CNN-SVM model significantly outperforms the standalone CNN and SVM models, achieving a high detection accuracy of 95.2%. This hybrid method offers a promising direction for enhancing the precision of computer-aided diagnosis systems, thereby contributing to more effective and reliable liver cancer detection methods that can aid healthcare professionals in making informed and timely decisions.

Keywords: Liver Cancer, Machine Learning, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Hyper-Fusion, Early Detection, Medical Imaging.
Scope of the Article: Public Health

Download PDF

The post B105105020125 appeared first on International Journal of Preventive Medicine and Health (IJPMH).

B105005020125

IJPMH Journal — Fri, 24 Jan 2025 09:36:37 +0000

Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8
K. Ramanandhini¹, Pandiarajan S²

¹Mrs. K. Ramanandhini, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.

²Mr. Pandiarajan S, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.

Abstract: This study examines the YOLOv8 method, a widely used object detection model, for detecting abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of-care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases

Keywords: YOLOv8 Method, Abdominal Diseases, CT Scans, Radiologists.
Scope of the Article: YOLOv8 Method

Download PDF

The post B105005020125 appeared first on International Journal of Preventive Medicine and Health (IJPMH).

B104905020125

IJPMH Journal — Fri, 24 Jan 2025 09:32:08 +0000

Early Detection and Intervention for Children’s Mental Health Issues Using Machine Learning
Mohamed Safdar B¹, Pandiarajan S²

¹Mr. Mohamed Safdar B, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.

²Mr. Pandiarajan S, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.

Abstract: The rise of mental health problems in children has created a need for early detection and intervention strategies. The routine method of diagnosing mental illness in children often relies on testing, which can lead to delays in treatment. Machine learning (ML) has become a powerful tool for analysing complex data, enabling the identification of subtle patterns associated with mental health. This article examines the potential of machine learning models for the early detection of mental health issues in children, with a focus on the accuracy of findings, timeliness of intervention, and ethical considerations related to data privacy and algorithmic bias.

Keywords: Mental Health, Machine Learning, Timeliness of Intervention.
Scope of the Article: Public Health

Download PDF

The post B104905020125 appeared first on International Journal of Preventive Medicine and Health (IJPMH).

B104805020125

IJPMH Journal — Fri, 24 Jan 2025 09:24:25 +0000

A Comprehensive Survey of Deep Learning and Ensemble Techniques in Glaucoma Detection
Mithunavarshini A.P¹, Deepa S²

¹Mithunavarshini A.P, Department of Computer Science and Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

²Deepa S, Assistant Professor (Sl. G), Department of Computer Science and Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

Abstract: Glaucoma stands as a primary contributor to irreversible blindness, necessitating precise and prompt diagnoses for effective management. Recent progress in deep learning, particularly through the use of ensemble methods involving Convolutional Neural Networks (CNNs), has demonstrated considerable potential in automating the detection of glaucoma by analyzing ocular imaging data, such as fundus and Optical Coherence Tomography (OCT) images. This survey offers a comprehensive overview of the latest ensemble-based approaches developed for glaucoma detection, highlighting the benefits of integrating various CNN architectures, including ResNet, VGG, and DenseNet, to enhance feature extraction and classification capabilities. The paper examines current trends in transfer learning, multimodal data integration, and hybrid methodologies that enhance the performance and adaptability of ensemble methods in clinical environments. Additionally, it addresses challenges such as the need for high-quality labelled datasets, model interpretability, and generalisation across different populations. By exploring recent studies, the survey aims to identify limitations in existing systems and propose advancements in ensemble-based glaucoma detection, ultimately offering valuable insights into a future research path that can narrow the gap between experimental findings and practical clinical applications.

Keywords: Glaucoma Detection, Deep Learning, Ensemble Methods, Convolutional Neural Networks, Optical Imaging.
Scope of the Article: Clinical Environments

Download PDF

The post B104805020125 appeared first on International Journal of Preventive Medicine and Health (IJPMH).

B104705020125

IJPMH Journal — Fri, 24 Jan 2025 09:02:18 +0000

Early Detection of Breast Cancer: Comparative Analysis of Machine Learning and Deep Learning Algorithms
Jashanpreet Singh¹, Syed Raahat², Aditya Sharma³, Sourabh Dellu⁴, Saksham Arora⁵

¹Jashanpreet Singh, Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

²Syed Raahat, Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

³Aditya Sharma, Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

⁴Sourabh Dellu, Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

⁵Saksham Arora, Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

Abstract: Breast cancer classification remains a critical challenge in medical diagnostics due to the imbalanced nature of available datasets, where the minority (cancerous malignant) class is often overshadowed by the majority (benign) class. This study proposes a hybrid model based on logistic regression, enhanced with class balancing techniques and ant search optimization, to improve the identification of the malignant class. The model is compared with SVM, Random Forest, and KNearest Neighbours (KNN) across three stages: prediction before diagnosis, at diagnosis and therapy, and post-treatment outcomes. The experiments, conducted on the Jupyter platform using the Wisconsin breast cancer dataset, demonstrate that the hybrid model achieves a high accuracy of 92.98%, significantly reducing the number of false negatives. The study highlights the strengths of logistic regression in providing interpretable results, which are crucial for clinical decision-making, especially when compared to more complex models, such as Artificial Neural Networks (ANN). This research offers a reliable and accurate tool for early breast cancer detection and prognosis, contributing to ongoing efforts to enhance patient outcomes through the integration of hybrid machine learning models in medical diagnostics.

Keywords: Breast Cancer, Wisconsin Breast Cancer Diagnosis (WBCD) Dataset, Machine Learning, Naïve Bayes Algorithm (NB), Support Vector Machine, Random Forests, Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR).
Scope of the Article: Public Health

Download PDF

The post B104705020125 appeared first on International Journal of Preventive Medicine and Health (IJPMH).

Volume-5 Issue-2, January 2025 – International Journal of Preventive Medicine and Health (IJPMH)

B105105020125

Share this entry

B105005020125

Share this entry

B104905020125

Share this entry

B104805020125

Share this entry

B104705020125

Share this entry