Background: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy. Although they cannot substitute the work of physicians in the prediction and diagnosis of disease, they can be of great help in identifying hidden patterns based on the results and outcome of disease. Methods: In this research, we retrieved the PIMA dataset from the Kaggle repository, the retrieved dataset was further processed for applied PCA, heatmap, and scatter plot for exploratory data analysis (EDA), which helps to find out the relationship between various features in the dataset using visual representation. Four different ML algorithms Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), and Logistic regression (LR) were implemented on Rattle using Python for the prediction of diabetes among the female population. Results: Results of our study showed that RF performs better in terms of accuracy of 80%, precision of 82%, error rate of 20%, and sensitivity of 88% as compared to other developed models DT, NB, and LR. Conclusions: Diabetes is a common problem prevailing across the globe, ML-based prediction models can help in the prediction of diabetes much earlier before the worsening of the condition.

Background: Insecticides are the chemical substances used to control pest in agriculture and domestic purpose, structural variations among insecticides leads to the change in the safety profile and application, in silico molecular docking studies are used to explain the mechanism of action of chemical substance on the specific target. Principal component analysis is a statistical method used to extract unique features from the large dataset. Methods: We made an investigation to find out correlation on the safety profile of insecticides in terms of acute toxicity, irritant, health hazard, and environmental hazard using principal component analysis (PCA), a dataset was prepared from available literature for 55 insecticides, their safety profile were retrieved from PubChem, PCA was performed on the prepared dataset using excel state software. ADME analysis was performed on the pyrethroids group of compounds using an online server-based docking as well as we had also performed molecular docking on the pyrethroids group of compounds cypermethrin, allethrin, resmethrin, and permethrin insecticides, with voltage-gated sodium ion channel and caspase-3 protein was retrieved from protein data bank PDB ID 2A79, and 1RE1, molecular docking was carried out using Auto Dock vina 4.2, discovery studio and open babel software. Results: Library of 55 insecticidal compounds was prepared their safety profile was obtained and PCA was performed on the dataset which shows that irritant and environmentFal hazard properties of insecticides largely affect the dataset. Server-based ADME analysis showed that cypermethrin is the least toxic compound among different pyrethroids. On the basis of In silico molecular docking permethrin was found to have maximum binding affinity -7.3 kcal/mol followed by cypermethrin, resmethrin, -7.2 kcal/mol, and the least binding affinity was observed with allethrin for 2a79 receptor -5.5 kcal/mol. Molecular docking on caspase-3 enzyme shows that maximum enzyme inhibition was achieved with cypermethrin and resmethrin -7.6 kcal/mol followed by permethrin -6.4and least with allethrin 6.1 kcal/mol. Conclusion: Multivariate PCA showed that the environmental hazard and irritant properties are predominantly present among different classes of insecticides, ADME analysis showed that cypermethrin is the least toxic compound to human as compared to other pyrethroids, and in silico studies showed maximum activity with permethrin for 2a79 receptor and cypermethrin and resmethrin for 1RE1 receptor.