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Dec 2025 DOI 10.14302/issn.2641-4538.jphi-25-5886
Y. Talbert PatriciaCorresponding author
Objective Diabetes poses significant public health challenges, with many individuals remaining undiagnosed and at risk of complications. This study aimed to evaluate the performance of decision tree ensemble methods for predicting diabetes onset using the Framingham Heart Study Teaching Dataset and to explore sex-specific risk patterns relevant to AI-driven interventions. Methods We analyzed data from 11,627 participants, incorporating demographics, vital signs, smoking status, medication use, and laboratory measures. Random Forest classifiers were developed to predict diabetes incidence at approximately 6-year (Period 2) and 12-year (Period 3) follow-ups. Class imbalance was addressed using undersampling, oversampling, and the Synthetic Minority Over-sampling Technique (SMOTE). Results The models demonstrated robust performance, achieving an Area Under the Curve (AUC) of 0.856 in Period 2, and moderate predictive ability in Period 3 (AUC = 0.732 in males, 0.786 in females). Key predictors included glucose level, BMI, systolic blood pressure, age, and heart rate. Notably, differences emerged in predictive accuracy between men and women, suggesting potential sex-specific vulnerabilities that merit further study. Conclusion Machine learning approaches, particularly Random Forests, show promise for medium- and long-term diabetes risk prediction, supporting early identification and intervention efforts. Future work should focus on hyperparameter tuning and explainability techniques, such as SHapley Additive exPlanations (SHAP) values, to improve model precision, interpretability, and fairness. Equity-focused strategies remain critical to ensure AI-driven tools benefit diverse populations and do not exacerbate existing disparities in diabetes care.
May 2024 DOI 10.14302/issn.2470-5020.jnrt-24-5100
T. Adebisi AbdulyekeenCorresponding author
Exploring the dynamic dimension of functional connectivity in dementia, this article departs from traditional static studies to capture the ever-changing brain networks. Investigating temporal connectivity patterns yields valuable insights into disease progression, individualized treatment, and early intervention. Additionally, the concept of cognitive reserve, therapeutic interventions, and machine learning integration are pivotal in revolutionizing dementia research and care.
May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058
Shrestha SwatiCorresponding author
Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.
Dec 2018 DOI 10.14302/issn.2641-5526.jmid-18-2488
Luis Fernández-Martínez JuanCorresponding author
Group of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, Universidad de Oviedo, Oviedo, Asturias, Spain
Background: Triple Negative Breast Cancer (TNBC) is a type of breast cancer with very bad prognosis. Predicting the histological grade (HG) and the lymph nodes metastasis is crucial for developing more suitable treatment strategies. Methods: We present the main clinical and pathological variables to predict the histological grade and lymph nodes metastasis via novel machine learning techniques. These variables are currently being used for prognosis and treatment in medical practice. This analysis was performed using a database of 102 Caucasian women diagnosed with TNBC. The results were cross-validated using random simulations of this dataset. Results: HG was predicted with an accuracy of 93.8% using a list of 6 prognostic variables with significant implications: Ki67 expression, use of Oral contraceptives, Col11A1 expression, Col11A1 score, E-cad truncated and Tumor size. The lymph nodes metastasis was predicted with an accuracy of almost 85% using only 6 prognostic variables: Vascular invasion, Tumor size, Perineural invasion, Age at diagnosis, Ki67 expression, and Col11A1 score. This analysis also served to establish the median signatures of the groups with and without lymph node metastasis, and proved the existence of a kind of small-size tumors (around 2.15 cm) with lymph node metastasis but not showing vascular and perineural invasions and higher protein Col11A1 score. Besides, these signatures proved to be very stable. Conclusions: The additional information conveyed by the prognostic variables found in these two classification problems provides new insight about the genesis and progression of this disease and can be used in medical practice to improve decisions in patient diagnosis and further treatment.