A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

Stability Indicating RP-UPLC Method for Assay of Emtricitabine and Tenofovir Disoproxil Fumarate in Bulk and Dosage Forms

A simple, sensitive and rapid stability indicating reverse phase ultra performance liquid chromatography (RP-UPLC) method was developed and validated for the determination of Emtricitabine (EMT) and Tenofovir disoproxil fumarate (TDF) in pure and tablet dosage forms. The chromatographic separation was achieved by using Waters (Alliance) UPLC system equipped with auto-sampler and photo diode array detector. A volume of 5 μL of standard or test was injected into the column and the components were separated by using the mixture of 0.68% potassium dihydrogen orthophosphate buffer of pH = 6 and methanol in the ratio 45:55 v/v as mobile phase at a flow rate of 1.2 mL/min through BEH C18 (100 mm × 2.1, 1.8 μm) at ambient temperature and were detected at a wavelength of 261 nm. System suitable parameters such as plate count and tailing factor for EMT and TDF were found to be 2427 & 3685, 1.16 & 1.23 respectively, and resolution between EMT and TDF peaks was found to be 3.12. The chromatographic parameters like retention time, peak area and peak height of EMT and TDF were found to be 0.684 & 0.930, 694,200 & 8,778,000 and 272,881 & 3685 respectively. Percent of assay of EMT and TDF in bulk and dosage forms was determined and found to be 101.48 and 103.22 respectively. Study of degradation was examined and found that the drugs were stable under degradation conditions. The present method was developed keeping the principles of green chemistry by using eco-friendly solvent methanol in mobile phase. The developed method was found to be simple, rapid and applied for the analysis of Truvada;therefore the proposed method is recommended for the analysis of EMT and TDF in pure and tablet dosage forms in any quality control laboratories.