@article{ author = {Hedyehzadeh, Mohammadreza and Yousefi, Mahdi}, title = {Application of Machine Learning Methods to Predict the Survival Rate of Glioblastoma Patients Using MR Images}, abstract ={Introduction: In this study, a method for automatic prediction of the survival rate of patients with glioblastoma tumor based on machine learning methods and MRI images is presented. Method: The data set used in this study is the BraTS 2017 database with 163 samples. Each sample of database images has four different imaging modalities as well as information such as the patient's overall life expectancy according to the patient's day and age. Database images are labeled into three categories, short-term, medium-term, and long-term, based on patient longevity after treatment. To improve the prediction results, different types of features were extracted and taught by different machine learning methods. The considered features include texture, volumetric, statistical, and deep features. The machine learning methods used include support vector machine, nearest neighbors, linear discriminant analysis, and decision tree. Results: The best prediction accuracy based on the classification was obtained using deep features extracted by a pre-trained convolutional neural network (CNN) and by linear discriminant analysis. Conclusion: Deep learning approaches showed a good performance in the prediction of medical parameters such as survival rate time.}, Keywords = {Survival Time, Deep Learning, Glioblastoma, Classification, MRI}, volume = {11}, Number = {1}, pages = {1-13}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.09}, url = {http://jhbmi.ir/article-1-828-en.html}, eprint = {http://jhbmi.ir/article-1-828-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Mohammadi, Alireza and Nasiri, Parya and Moghabeli, Roy}, title = {Spatio-temporal Analysis of Asthma Prevalence in the Tehran Metropolis Using Geographic Information Systems (GIS)}, abstract ={Introduction: Tehran, one of the world's most polluted cities, faces numerous health challenges, including respiratory diseases. Identifying high-risk areas for specific diseases at different periods is crucial for effective health management within the community. Method: This applied and descriptive-analytical study employed spatial statistics and GIS to conduct a spatio-temporal analysis of asthma in the Tehran metropolis. The statistical population comprised 1,473 individuals suffering from asthma across 355 neighborhoods within Tehran's legal boundaries between 2017 and 2020. To identify and understand the spatial patterns of asthma, techniques such as central mean and standard deviation ellipse, nearest neighbor index, hot spot analysis, global and local Moran's index, and kernel density estimation were utilized to analyze the spatial distribution patterns within the GIS environment. Results: The findings revealed a uniform nearest neighbor index in the spatial pattern analysis. The kernel density estimation model showed that in 2017, the highest asthma distribution was observed in the southwestern neighborhoods of Tehran. In 2018, the concentration shifted to the center, southeast, and southwest regions. In 2019, the southeastern areas were most affected, while in 2020, the southeastern and southern parts of Tehran exhibited the highest asthma prevalence. Conclusion: A comprehensive understanding of asthma hotspots, spatial patterns, and geographical distribution of health has been achieved at the neighborhood level in Tehran, which can be considered in preventive measures.}, Keywords = {Spatio-temporal analysis, asthma, geographic information system (GIS), Tehran city}, volume = {11}, Number = {1}, pages = {14-25}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.10}, url = {http://jhbmi.ir/article-1-833-en.html}, eprint = {http://jhbmi.ir/article-1-833-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Motamed, Sara and Askari, Elham}, title = {Diagnosing Heart Disease Using Convolutional Neural Network and the Particle Swarm Optimization}, abstract ={Introduction: The human heart is a physiologically vital organ whose signals can be continuously recorded using an electrocardiogram (ECG) device. Cardiovascular diseases are one of the leading causes of mortality worldwide. Timely and accurate identification of this condition, along with preventive measures, can help reduce the incidence of cardiovascular diseases. Method: This article aimed to predict a group of heart failures based on the patterns found in extracted features from patients with cardiac arrhythmias, distinguishing them from samples in a normal state. The proposed model involves preprocessing operations, such as discretization and replacement of missing values using column-wise averaging, on the dataset. Subsequently, feature selection operations were performed on normalized data to reduce complexity and improve speed and accuracy. The data is then fed into decision tree classifiers, k-nearest neighbors, naive Bayes, and convolutional neural networks. Results: A comparison of the accuracy obtained from different algorithms before and after applying the proposed method reveals improved performance across all methods after implementation. Particularly, the convolutional neural network demonstrates superior performance. Conclusion: Based on the results, it can be concluded that the proposed model achieves an accuracy of 92.34%, surpassing other methods.}, Keywords = {Cardiac Arrhythmia, Convolutional Neural Network, Particle Swarm Optimization, Multiclass Classification}, volume = {11}, Number = {1}, pages = {26-42}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.11}, url = {http://jhbmi.ir/article-1-840-en.html}, eprint = {http://jhbmi.ir/article-1-840-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Oviesi, Safura and Tarokh, Mohammad Jafar and Momeni, Mohammad Kazem}, title = {Automatic Diagnosis of Pulmonary Emphysema Using Optimized Unet-based Deep Neural Network}, abstract ={Introduction: Pulmonary emphysema is one of the lung diseases that usually remains unknown until old age and does not have a definitive treatment. A quick diagnosis of this disease helps a lot to the people involved in this disease and prevents the growth of emphysema masses. This research tries to in early diagnosis of this disease with the help of deep learning methods. Method: This research tries to diagnose this disease faster with the help of Unet neural network optimized with GPC meta-heuristic algorithm. The data of this research were collected from Imam Ali and Bu Ali Sina hospitals, Zahedan city, Sistan and Baluchistan province. The data include 300 pieces with emphysema, including 65 cases of CLE, 97 cases of PSE, 138 cases of PLE, and 45 cases of normal data. These data were analyzed by Unet deep neural network and GPC optimization algorithm, and finally, with the help of accuracy criteria, recall, specificity, and F-measure were compared and investigated with other methods. Results: In this research, the criteria used have much better results compared to other emphysema diagnosis methods with the help of the optimized Unet network, with accuracy of 18.97, prediction of 40.98, sensitivity of 48.23, and f score of 97.50, respectively, which shows a faster, more accurate, and more effective diagnosis of this disease with the help of the proposed method. Conclusion: Using the right deep learning methods in combination with strong optimization algorithms can enable faster and more accurate treatment of diseases.}, Keywords = {Artificial intelligence, Neural networks, Pulmonary emphysema, Image processing, Deep Learning}, volume = {11}, Number = {1}, pages = {43-59}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.12}, url = {http://jhbmi.ir/article-1-841-en.html}, eprint = {http://jhbmi.ir/article-1-841-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Afrasiabi, Mahlagha and Movahedi, Ahm}, title = {Alzheimer\'s Prediction Using Deep Neural Network and Particle Swarm Optimization}, abstract ={Introduction: Alzheimer's disease is an irreversible neurological condition characterized by cognitive, behavioral, and memory impairments. Early prediction before the transition from mild cognitive impairment to Alzheimer's disease is still a challenging issue. This study aimed to identify factors associated with Alzheimer's disease. Method: This study proposes a framework for predicting Alzheimer's disease using data collected from the OASIS project, made available by the Washington University Research Center. In this study, a deep neural network was used for prediction. A particle swarm optimization (PSO) algorithm was employed for selecting appropriate features. The combination of these two methods increases the accuracy of the proposed prediction method. Results: The results indicate that the proposed method achieves higher accuracy with fewer features. Among the 11 features in this dataset, six features (age, socioeconomic status, Mini-mental state examination score, clinical dementia rating scale, estimated total intracranial volume, and normalized whole-brain volume) have a significant impact on predicting the disease. Among these six features, the clinical dementia rating scale is of great importance. Conclusion: This study investigated the influential factors and prediction of Alzheimer's disease. Early diagnosis of Alzheimer's disease allows for the provision of appropriate diagnostic and therapeutic services, as well as an improvement in patients' quality of life. The proposed method in this study is compared with various machine learning algorithms that have shown good accuracy in predicting Alzheimer's disease. The results indicate that the accuracy of the proposed method is higher with fewer features.}, Keywords = {Alzheimer's disease, deep neural network, machine learning, particle swarm optimization}, volume = {11}, Number = {1}, pages = {60-71}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.13}, url = {http://jhbmi.ir/article-1-849-en.html}, eprint = {http://jhbmi.ir/article-1-849-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Einipour, Ami}, title = {Providing a Multi-Scale Self-Encoding Method to Improve Clustering and Analysis of Single-Cell Data}, abstract ={Introduction: In bioinformatics, analyzing single-cell data is crucial for understanding cellular functions' complexities. However, this analysis faces challenges like inefficient dimensionality reduction and suboptimal clustering. This study aimed to present a method that enhances the clustering of single-cell data, improves reconstruction quality, and reduces data dimensions. Method: This paper introduces SAMS (Single-cell Analysis using Multi-Scale Autoencoder), which uses a multi-scale autoencoder model to improve the challenges in single-cell data analysis. The SAMS method involves three primary steps: (1) data preprocessing and normalization, (2) employing a deep neural network model to reconstruct and reduce data dimensions with the help of a multiscale autoencoder, and (3) clustering the reduced data using the K-means algorithm to assess the method's performance. Results: The SAMS method was implemented using Python on single-cell datasets. The results demonstrate that SAMS can effectively visualize cells in a two-dimensional space with an average Nearest Neighbor Error (NNE) rate of 89%, indicating a strong preservation of data structure. Additionally, the Silhouette index and Davis-Bouldin index, which measure clustering accuracy, show significant improvement with averages of 0.66 and 0.50, respectively. Conclusion: The proposed SAMS method by combining the multiscale self-encoder model and the K-means algorithm could obtain better results than the previous methods. Its application in single-cell data analysis can aid researchers in gaining deeper insights into cellular functions and discovering new patterns.}, Keywords = {Single-cell analysis, Dimensionality reduction, Clustering analysis}, volume = {11}, Number = {1}, pages = {72-82}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.14}, url = {http://jhbmi.ir/article-1-867-en.html}, eprint = {http://jhbmi.ir/article-1-867-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Kanani, Shiva and Mahdavi, Iraj and Ziaie, Naghmeh and RahimpourCami, Bagher}, title = {Prediction of 30-day Mortality for Heart Failure Patients with Cardiogenic Shock Using Random Forest Hyperparameter Tuning}, abstract ={Introduction: Heart failure is a clinical syndrome resulting from structural or functional abnormalities of the heart, leading to reduced cardiac output or increased intracardiac pressure. When combined with cardiogenic shock, it becomes an emergency condition with a high mortality rate, necessitating immediate diagnosis and treatment. Accurate prediction of 30-day mortality in these patients is vital for timely care and patient survival. This study aimed to optimize the Random Forest algorithm by adjusting hyperparameters to more accurately predict 30-day mortality in heart failure patients with cardiogenic shock. Method: In this research, data from 201 cardiac patients aged over 18 years who experienced cardiogenic shock at Rouhani Hospital in Babol in 2020, were used. Thirty-four selected features such as age, history of cardiac surgery, pH, lactate levels, diabetes, etc., were examined, and their one-month mortality was tracked through telephone follow-ups. Results: The results showed that increasing age (above 57 years), decreasing pH (below 7.3), and elevating lactate levels (above 2) significantly increased the risk of 30-day mortality. By optimizing the hyperparameters of the Random Forest algorithm (ntree=1000 and mtry=14), prediction accuracy improved from 66.0% to 71.8%. Conclusion: This study demonstrates that the accuracy of the Random Forest algorithm depends on its input hyperparameters and that optimizing these parameters can lead to a more precise prediction of mortality in heart failure patients with cardiogenic shock. With appropriate optimization, this algorithm can serve as an effective tool for the early detection of high-risk patients and timely provision.}, Keywords = {Heart Failure, Cardiogenic Shock, Random Forest, Hyperparameter Tuning, Mortality prediction}, volume = {11}, Number = {1}, pages = {83-95}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.15}, url = {http://jhbmi.ir/article-1-868-en.html}, eprint = {http://jhbmi.ir/article-1-868-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Abdekhoda, Mohammadhiw}, title = {Article Retraction: A Tool to Deal with Information Pollution}, abstract ={The article is a letter to the editor, so it does not have an abstract.}, Keywords = {Article Retraction, Information Pollution}, volume = {11}, Number = {1}, pages = {96-100}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.16}, url = {http://jhbmi.ir/article-1-884-en.html}, eprint = {http://jhbmi.ir/article-1-884-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} }