This study presents an analysis of consumer opinions on waste medicine management. The study explores consumers' concerns, preferences, and suggestions regarding correctly disposing unused or expired medications. The analysis shows the key points that emerged from consumer opinions, including environmental impact, public health and safety, accessibility and affordability, education and awareness, pharmaceutical industry responsibility, convenience and ease of disposal, privacy and confidentiality, community engagement, alternatives to disposal, extended producer responsibility, international collaboration, technology solutions, environmental stewardship, and government regulation and support. This study shows the importance of understanding consumer perspectives in developing effective waste medicine management strategies prioritizing environmental sustainability, public health, and consumer satisfaction. We used the multi-criteria decision-making (MCDM) methodology to deal with these criteria. We gathered 15 criteria concerned with waste medicine management. We used the DEMATEL method to show the criteria weights and relationships between criteria. The DEMATEL method is integrated with the single-valued neutrosophic set to deal with uncertain data. The results show the environmental impact has the most significant weight.

In the medical field, detecting skin cancer from pictures can be difficult. Skin cancer identification is a laborious process in modern medicine that could eventually result in patient death. Early detection of skin cancer is essential to the effectiveness of full treatment. It is difficult to diagnose skin cancer effectively. Consequently, there are not enough qualified dermatologists around the globe to meet the demands of modern healthcare. Data imbalance problems are caused by the wide diversity in data from several healthcare industry categories. Deep learning models are frequently trained in one category more than others due to issues with data imbalance. Using an unbalanced dataset, this study suggests a revolutionary deep learning-based skin cancer detector. Data augmentation was used to balance different skin cancer categories and overcome data imbalance. The MNIST: HAM 10000 skin cancer dataset, consisting of seven skin lesion categories, was used. Deep learning models are widely used in image-based disease diagnosis. Deep learning-based models (Custom CNN, DenseNet201, ResNet-101, and Xception) have been used to classify skin cancer. The proposed framework was fine-tuned using different sets of hyperparameters. The results show that Xception outperformed ResNet-101, DenseNet201, and Custom CNN in terms of accuracy, Fl score, and receiver operating characteristic (ROC) curve. It achieved an accuracy of 0.9148. Our proposed framework could help in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare workers.

The paper discusses an improved phishing detection system that uses hybrid machine learning for analyzing URL-based features. Further improvement of prior work using better feature selection as well as ensemble methods is discussed. The proposed model enhances classification accuracy, precision, and recall by utilizing an innovative hybrid ensemble approach that integrates Logistic Regression, Support Vector Machine, and Decision Tree, alongside newly incorporated evaluation techniques. The results manifest high improvement in performance metrics as compared to the previous methods. Various comparisons and benchmarks with other methods have further proved the robustness of our proposed system for detecting malicious URLs.

Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision-making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. performance of the proposed ML algorithm was estimated using numerous conditions to recognize the most suitable machine learning algorithm in the class of models. RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.

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