Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease.

Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes.

Let A be a group, ⟨t⟩ the free group generated by t, and let r(t) ∈ A∗ ⟨t⟩. The group equation r(t) = 1 is said to have a solution over A if a solution exists in some group containing A. In other words, this means that the canonical map A → is injective. A group is said to be torsion-free if the order of every non-identity element is infinite. There is a conjecture (attributed to F. Levin) that every group equation over an arbitrary torsion-free group is solvable. It has been proved that this conjecture holds true for group equations of length at most seven. This study examines the solvability of group equations of length eight over a torsion-free group

Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potential to process complex datasets and support decision-making in OC diagnosis. Nevertheless, traditional ML models tend to be biased, overfitting, noisy, and less generalized. Moreover, their black-box nature reduces interpretability and limits their practical clinical applicability. In this study, we introduce an explainable ensemble learning (EL) model, TreeX-Stack, based on a stacking architecture that employs tree-based learners such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost) as base learners, and Logistic Regression (LR) as the meta-learner to enhance ovarian cancer (OC) diagnosis. Local Interpretable Model-Agnostic Explanations (LIME) are used to explain individual predictions, making the model outputs more clinically interpretable and applicable. The model is trained on the dataset that includes demographic information, blood test, general chemistry, and tumor markers. Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7. Relevant features are then selected using the Boruta feature selection method. To obtain robust and unbiased performance estimates during hyperparameter tuning, nested cross-validation (CV) with grid search is employed, and all experiments are repeated five times to ensure statistical reliability. TreeX-Stack demonstrates excellent diagnostic performance, achieving an accuracy of 0.9027, a precision of 0.8673, a recall of 0.9391, and an F1-score of 0.9012. Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4 (HE4) as the most significant biomarker for OC. The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.