Some Articles Published and Open Source Datasets Analyzed Using WisdomEra Systems
Prediction of Surgery Type for Uterine Fibroids Using Machine Learning Algorithms and Hormone Values
Aim: This study aimed to develop and externally validate machine learning (ML)-based models to characterize surgical classification patterns between hysterectomy and myomectomy using fibroid characteristics and female sex hormone profiles in women with uterine fibroids.
Statistical analyses and tools: Statistical analyses and ML training were performed using Wistats v3.0 (WisdomEra Corp., Istanbul, Turkey), which incorporates Python-based statistical and ML libraries (SciPy v1.2.3, scikit-learn v0.24.0, statsmodels v0.9.0).
This study was conducted using WisdomEra analytics products (Wistats).
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HbA1c as a Key Metabolic Marker in Predicting Myomectomy Requirement in Women with Uterine Fibroids: A Machine Learning Study
Aim: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting myomectomy requirement remains unclear. This study aimed to evaluate the predictive role of HbA1c for myomectomy requirement in women with uterine fibroids using conventional statistical analyses and machine learning-based models under real-world clinical decision-making conditions.
Statistical Tools: All statistical analyses and machine learning procedures were conducted using Wistats v3.0 (WisdomEra Corp., Istanbul, Turkey), incorporating Python v2.7.14-based libraries including SciPy, scikit-learn, and statsmodels.
This study was conducted using WisdomEra analytics products (Wistats).
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An AI-Driven Clinical Decision Support Framework Utilizing Female Sex Hormone Parameters for Surgical Decision Guidance in Uterine Fibroid Management
Aim: To develop a clinical decision support algorithm predicting surgical necessity by integrating fibroid characteristics and female sex hormone levels.
Statistical Tools: Wistats v3.0 (SciPy, scikit-learn, statsmodels), Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlations, logistic regression, ML classifiers.
This study was conducted using WisdomEra analytics products (Wistats).
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Are the Frequency and Severity of COVID-19 Infection Higher in Cancer Patients Than in the General Population?
Aim: To investigate whether cancer increases the frequency and severity of COVID-19 infection.
Statistical Tools: W-Analyzer v1.4.53 (SciPy v1.2.3), independent samples t-test, Chi-square test.
This study was conducted using WisdomEra analytics products (W-Analyzer).
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