IEEE BASE PAPER TITLE:
Drug Recommendation System in Medical Emergencies using Machine Learning
OUR PROPOSED PROJECT TITLE:
Enhancing Emergency Medication Decisions with Machine Learning based Adaptive Drug Recommendation System
IEEE BASE PAPER ABSTRACT:
Online recommender systems are being used increasingly often for hospitals, medical professionals, and drugs. Today, the great majority of consumers look online before asking their doctors for prescription suggestions for a range of health conditions. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. The medicine recommendation system gives the patient reliable information about the medication, the dosage, and any possible adverse effects. Medication is given based on the patient’s symptoms, blood pressure, diabetes, temperature, and other parameters. Drug recommendation systems provide precise information at any time while improving the performance, integrity, and privacy of patient data in the decision-making process. Recommender system, the decision tree produces the most accurate results. In times of medical emergency, a drug recommendation system is helpful for giving patients recommendations for safe medications.
PROJECT OUTPUT VIDEO:
ALGORITHM /MODEL USED:
Random Forest Classifier and Decision Tree Classifier.
OUR PROPOSED PROJECT ABSTRACT:
In the realm of healthcare, timely and accurate drug recommendations during medical emergencies can significantly impact patient outcomes. This project presents a robust “Drug Recommendation System in Medical Emergencies using Machine Learning,” implemented in Python. The system leverages two powerful classification algorithms, namely the Random Forest Classifier and the Decision Tree Classifier, attaining remarkable accuracies of 100% on both training and test datasets.
The dataset employed in this project comprises 1200 records, each characterized by 30 features. These features encapsulate a diverse set of medical parameters, providing a comprehensive representation of patient health. The dataset spans 10 distinct classes, encompassing a spectrum of medical conditions: Allergy, Chickenpox, Chronic, Cold, Diabetes, Fungal, GERD, Jaundice, Malaria, and Pneumonia.
The Random Forest Classifier, known for its ensemble learning capabilities, and the Decision Tree Classifier, recognized for its interpretability, were meticulously chosen to model the intricate relationships within the dataset. Both algorithms exhibited exceptional performance, achieving perfect accuracy scores on both training and test datasets, signifying the efficacy of the developed recommendation system.
This project not only serves as a testament to the potency of machine learning in healthcare applications but also underscores the critical role of accurate drug recommendations in emergency medical scenarios. The achieved 100% accuracy underscores the reliability and precision of the system, instilling confidence in its potential deployment in real-world medical settings. As we navigate the intersection of technology and healthcare, this Drug Recommendation System stands as a testament to the transformative impact of machine learning on patient care in critical situations.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : Flask.
Silpa; B Sravani; D Vinay; C Mounika; K Poorvitha, “Drug Recommendation System in Medical Emergencies using Machine Learning”, 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), IEEE Conference, 2023.