Predictive modelling and identification of key risk factors for stroke using machine learning.

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

Comparative conventional preventive strategies for insect pest of okra.

The use of natural substances for pest control in agriculture is economically viable. It benefits both the human being and the environment due to its low persistence and toxicity. Therefore, the biopesticidal potential of three- plants-derived extracts (clove [Syzygium aromaticum], Hing [Hing (Asafetida)], and Wood Ash [Eucalyptus globulas]) was evaluated against different ' 'insect's pests on five okra varieties. All the treatments were sprayed at two stages, 1st before flowering and 2nd at the fruit-bearing stage. The results of the 24 h pre-spray revealed that the mean density of Aphis gossypii, Erias insulana, and Bemisia tabaci were significantly lower on a Shehzadi variety. However, among the treatments mean density of the A. gossypii and E. insulana after 1st and 2nd treatments were substantially more bass with E. globulas. Moreover, the Mean density of aphids was significantly lower after 72 h and 1-week time intervals. Furthermore, after 1st and 2nd treatments, the B. tabaci was considerably lower with hing on Shehzadi variety. It was found in the present study that the yield of five okra varieties was affected significantly by the application of the three treatments-pesticides. Among the various treatments, the application with E. globulas recorded a considerably higher crop yield. Therefore, clove, hing and E. globulas could be effective as alternative pest management methods. Furthermore, biopesticides generally are encouraged since they can proffer the solution of controlling insect pests without any environmental concern.