Volume 5, Issue 1

February 2025 · 2 published papers

IJASD

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Drug Utilization Evaluation Among Hypertensive Patients In A Tertiary Care Setting Of Southern Rajasthan

Attahir Saad Ayuba

Hypertension, a prevalent cardiovascular condition, is a significant public health concern globally. This burden is particularly pronounced in India, affecting a substantial proportion of the population. Effective management of hypertension relies heavily on the appropriate utilization of antihypertensive medications. This prospective observational cohort study, conducted in a tertiary hospital in Southern Rajasthan, recruited 220 hypertensive patients on antihypertensive drug therapy and followed them at intervals of 0, 24 hours, 7 days, and 30 days. The study found that angiotensin receptor blockers (ARBs) were the most frequently used class of drugs in monotherapy, accounting for 15%. In dual therapies, ARB combined with calcium channel blockers (CCBs) was used by 20% of patients, while 11% of patients were on triple therapy involving ARB, CCB, and diuretics.

February 22, 2025 · Volume 5, Issue 1 · 10.66917/ijasd.a000017

IJASD

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Ai Explainability In Insurance Applications With Telematics Data

Dr. Rajkumar J. Bhojan, Dr. Ramyachitra, Dr. Subra Ganesan, Ragavi.r, Roshini Rajkumar

This paper describes a step-by-step process of creating an insurance-based application that predicts insurance premium using Artificial Intelligence (AI) method of machine learning (ML). Artificial Intelligence technologies will have a high impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Nowadays, AI is being used in insurance claims processing, underwriting, fraud detection, anomaly detection and customer service. In this research paper, we are going to predict a driver's premium charges based on the driver's driving behavior. Here, we used telematics, machine learning, and deep learning technologies for predicting the insurance premium cost in the auto insurance space. In this research journey, we propose a hybrid framework architecture with multiple combinations of Supervised Learning (LR, DS, RFR and SVM) Algorithms and interpretability of AI model.

February 24, 2025 · Volume 5, Issue 1 · 10.66917/ijasd.a000011