Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn into malignant tumors. These cancer cells can metastasize to be life-threatening and fatal. An intricate mix of environmental factors and individual genetic composition can lead to the formation of this deadly carcinoma. Improvements in the diagnosis and treatment of cancer are essential given the rising incidence of breast cancer. Over the past few decades, machine learning has helped provide accurate medical diagnosis results. Therefore, this study used diagnostic characteristics of patients and multiple machine learning classifiers to identify breast cancer. Incorporating explainable artificial intelligence techniques revealed the underlying factors for the model predictions, adding a layer of transparency and interpretability. Out of the algorithms, random forest showed the best result, an F1-score of 84%. The stacked ensemble model, which combines the strengths of different models, obtained an F1-score performance of 83%. The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.

Smoking is a leading cause of various health conditions, including cancer and respiratory diseases. Smokers often face medical restrictions such as limitations in blood and organ donation, reduced effectiveness of medications, and increased surgical complications. These impacts underscore the need for early detection of smoking status to enable timely intervention. This study explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict smoking status based on health parameters, including biosignals and clinical biomarkers. A balanced subset of 2,000 instances was sampled from a publicly available Kaggle dataset comprising clinical and biometric features. Multiple ML models were implemented, including Random Forest Classifier, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, CatBoost Classifier, and an Artificial Neural Network. The Random Forest Classifier achieved the better performance with an accuracy of 0.80, precision of 0.80, recall of 0.80, and F1-score of 0.79. To enhance model interpretability, four Explainable Artificial Intelligence (XAI) techniques were applied: Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), QLattice, and Anchor. SHAP identified hemoglobin as the most influential predictor, while LIME, QLattice, and Anchor highlighted the role of gamma-glutamyl transferase (t). Interactions between hemoglobin, GTP, and height were associated with more accurate predictions. The integration of ensemble modeling and multiple XAI approaches offers deeper interpretability than prior studies, providing healthcare providers and policymakers with a robust, transparent decision-support tool for targeted intervention strategies.

Artificial intelligence (AI) technologies are used in healthcare to detect diseases faster, provide personalized treatment, automate some processes, and enhance hospital safety. Yet, one of the critical challenges is users’ hesitation and unwillingness to adopt technological applications in medical practice. Hence, this research aims to identify the important factors affecting professionals’ behavioural intentions to use AI in healthcare. This cross-sectional survey was conducted in Kerala, India. We proposed a framework grounded on the literature on technology acceptance theories, and the conceptual model was tested using a 20-item questionnaire with a 5-point Likert scale. The structured questionnaire was converted into a Google Form and shared with the respondents. A total of 347 respondents, including doctors, nurses, and allied healthcare professionals from 5 different private hospitals in India, participated in this study. The partial least square structural equation modelling approach was used to evaluate the proposed framework upon confirming the reliability and validity of the measurement items. The study findings reveal that perceived ease of use (PEU), perceived usefulness (PU), and facilitating conditions (FC) are the critical factors that influence the behavioural intentions (BI) of healthcare professionals to use AI. Though healthcare professionals perceived the risk of using AI, it did not affect their choice to use AI. This study gives an understanding of healthcare professionals’ behavioural intentions to use AI so that the administrators can plan and improve their strategies to implement AI in hospitals.

Low birthweight (LBW) is a significant health challenge worldwide, as these neonates experience both short- and long-term disabilities. Factors affecting maternal and fetal health during early to mid-pregnancy can greatly influence fetal development. Prediction of birthweight using machine learning (ML) models with antenatal data may help in better clinical management. However, the lack of explainability in these models has raised concerns within the medical community. To address this issue, our study aims to develop a more practical ML model by incorporating explainable artificial intelligence (XAI). We prospectively collected real-world clinical data of 19 maternal and fetal clinical features from 237 singleton pregnancies. Statistical analyses were conducted using Jamovi (version: 2.6.26) and JASP team (2024) JASP (version: 0.18.3). Multiple ML classifiers were employed. We developed a stacked ensemble model that integrated various algorithms, including a custom-stacked ensemble approach and three XAI methodologies: Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor. These methods provided meaningful explanations to help construct reliable and optimal clinical predictive models. Among the ML classifiers evaluated, the AdaBoost model achieved the highest performance, with a maximum accuracy of 77%, a precision of 73%, a recall of 77%, and an F1 score of 72%. The stacked model demonstrated an accuracy of 75%, indicating its possibility in clinical application. However, the accuracy of these models might be affected by the limited dataset, which included pregnant women undergoing treatment for thyroid abnormalities, diabetes, and hypertension. Our developed model identified several key attributes that influence birthweight, such as maternal height, nuchal translucency thickness, parity, crown-rump length, glycated hemoglobin, hypertensive disorders of pregnancy, and pregnancy-associated plasma protein A. This model can assist medical professionals in making more precise birthweight predictions using routinely collected antenatal parameters, enabling timely medical decisions and treatments.

Multiple sclerosis (MS) is a dangerous illness that strikes the central nervous system. The body’s immune system attacks myelin (an entity above the nerves) and impairs brain-to-body communication. To date, it is not possible to cure MS. However, symptoms can be managed, and treatments can be provided if the disease is diagnosed early. Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. Descriptive and inferential statistical analysis has been conducted before training the classifiers. The most essential markers were chosen using a technique called mutual information. Among the search techniques, the Bayesian optimization search technique prevailed to be pre-eminent, with an accuracy of 89%. To comprehend the diagnosis generated by the ML classifiers, four techniques of explainable artificial intelligence were utilized. According to them, the crucial attributes are periventricular magnetic resonance imaging (MRI), infratentorial MRI, oligoclonal bands, spinal cord MRI, breastfeeding, varicella disease, and initial symptoms. The models could be deployed in various medical facilities to detect MS in patients. The doctors could also use this framework to get a second opinion regarding the diagnosis.

Foetal outcomes with reduced foetal movements in the later pregnancy are widely reported. We intend to quantify early foetal movements (FMs) through a checklist and their foetal outcomes via explainable artificial intelligence. It is a prospective observational study of 356 foetuses in the first trimester, and we were able to screen only 230 foetuses for early foetal growth restriction (FGR). Of which 26 were FGR and 204 were normal and were identified from the dataset using non‐probability convenience sampling techniques. JASP 0.18.3, Jamovi 2.3.21, and Google Collaboratory were used to construct the predictive model. Ultrasound scores of more than 8 had favourable indicators of a normal foetus. CatBoost had the highest accuracy and recall of 87; the highest precision of 79 was given by random forest (RF), decision tree (DT), K‐nearest neighbour (KNN), and CatBoost; and the F1 score of 83 was given by CatBoost. The lowest Hamming loss of 0.13 was obtained via CatBoost. The highest Jaccard score of 0.87 was by CatBoost. The stacked model has an accuracy of 89, a precision of 79, and a recall of 83. Shapley additive explanations (SHAP), local interpretable model‐agnostic explanations (LIME), QLattice, and Anchor also provided good explanations. The created model can serve as a warning tool to obstetricians to make timely medical decisions.