Authors:
Aiym Temirbayeva*
Published Date: November 10, 2025
Cite: Temirbayeva A. Comparison of different machine learning methods for predicting heart disease. Crystal J Med Healthc. 2025;1(1): 01-08.
Abstract
Purpose: The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting—on a dataset containing clinical features of patients. The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis.
Methods: The study used a dataset of 270 patients with 13 clinical features. The data was pre-processed, and target variables were converted into binary values for classification. The dataset was split into training and test sets in a 70-30 ratio. Five machine learning models were trained and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Confusion matrices were analysed to gain additional insights into model performance.
Results: Logistic Regression and Random Forest achieved the best performance among the models, with accuracies of 86.4% and 80.2%, respectively. Logistic Regression demonstrated a ROC-AUC score of 0.844, while Random Forest scored 0.88. The confusion matrices highlighted the predictive strengths and limitations of each model.
Conclusion: Logistic Regression and Random Forest were identified as the most reliable models for predicting heart disease in this dataset. Future work will explore hyperparameter tuning and ensemble methods to further enhance model performance, providing valuable insights for early diagnosis and treatment of cardiovascular diseases.
Keywords
Heart Disease, Prediction, Machine Learning Methods, Logistic Regression, Accuracy
Authors:
Mohammad Ahmad Ahmad Odah*
Published Date: May 08, 2025
Cite: Odah MAA. Photosynthetic reprogramming enhancing carbon fixation in crops through synthetic biology. Crystal J Med Healthc. 2025;1(1): 01-10.
Abstract
Background: Photosynthesis is the foundation of plant productivity, yet its natural efficiency remains limited, with most staple crops operating under the suboptimal C3 pathway. Enhancing carbon fixation efficiency is a major goal in agricultural biotechnology, especially in the face of climate change and increasing food demands. Synthetic biology offers innovative strategies to reprogram photosynthesis, optimize RuBisCO function, and introduce synthetic carbon assimilation pathways.
Objective: This review explores the latest advancements in synthetic biology applied to photosynthetic reprogramming. It highlights the potential of genetic engineering to enhance carbon fixation, reduce photorespiration losses, and develop highyield, climate-resilient crops.
Methods: A comprehensive literature review was conducted, analyzing recent breakthroughs in RuBisCO optimization, synthetic carbon fixation cycles, and chloroplast genome engineering. Studies on engineered carbon-concentrating mechanisms (CCMs) and artificial CO₂ assimilation pathways were evaluated for their potential applications in agriculture.
Results: Synthetic biology approaches have demonstrated significant improvements in photosynthetic efficiency. Directed evolution has optimized RuBisCO activity, while the integration of bacterial and algal CCMs into C3 plants has increased CO₂ fixation rates. Additionally, synthetic pathways like the CETCH cycle show promise in surpassing the Calvin cycle’s efficiency.
Conclusion: The integration of synthetic biology into photosynthesis enhancement presents a transformative solution to improving crop productivity and climate resilience. While significant progress has been made, challenges remain in scaling these innovations to commercial agriculture. Future research should focus on refining metabolic engineering strategies, addressing regulatory concerns, and ensuring field applicability to maximize the impact of synthetic photosynthesis.
Keywords
Synthetic Biology, Photosynthetic Reprogramming, Carbon Fixation, RuBisCO Optimization, Climate-Resilient Crops, Metabolic Engineering
Authors: Mohammad Ahmad Ahmad Odah*
Published Date: April 30, 2025
Cite: Odah MAA. Mitochondrial epitranscriptomics: The role of RNA modifications in cellular energy regulation and aging. Crystal J Med Healthc. 2025;1(1): 01-08.
Abstract
Background: Mitochondria play a crucial role in cellular metabolism and energy production. While traditionally studied through nuclear and mitochondrial DNA regulation, recent research highlights the significance of post-transcriptional modifications in mitochondrial RNA (mtRNA). These modifications influence mitochondrial gene expression, energy balance, and aging. Understanding mitochondrial epitranscriptomics—specifically modifications like N6-methyladenosine (m6A), pseudouridylation, and 5-methylcytosine (m5C)—is essential for uncovering their impact on cellular function and disease.
Objective: This review aims to explore the emerging field of mitochondrial epitranscriptomics, analyzing key RNA modifications, their roles in oxidative phosphorylation (OXPHOS) and reactive oxygen species (ROS) regulation, and their implications in aging and age-related diseases. Additionally, we discuss advanced methodologies for studying these modifications and potential therapeutic strategies for mitochondrial dysfunction.
Conclusion: Mitochondrial RNA modifications represent a critical layer of gene regulation affecting cellular energy homeostasis and aging. Their dysregulation is associated with metabolic disorders, neurodegenerative diseases, and cellular senescence. Advances in RNA sequencing and epitranscriptomic research highlight the potential for therapeutic interventions targeting mitochondrial RNA modifications to mitigate mitochondrial dysfunction and promote longevity. A deeper understanding of these modifications could pave the way for novel approaches to treating age-related diseases and enhancing healthy aging.
Keywords
Mitochondrial Epitranscriptomics, RNA Modifications, Aging, Oxidative Phosphorylation, m6A, Pseudouridylation, m5C, Neurodegeneration, Metabolic Disorders, Longevity
