ABSTRACT
- Drug overdose is now the leading cause of death for those under 50 in the World.
- Inadequate data present a challenge for city officials, which prevents them from investigating the scale of the opioid overdose crisis.
- Various factors need to be considered in the prediction model for estimating the level of drug consumption, type of drug, and the location of the affected area.
- The aim of this project is to investigate several prediction and analysis models for forecasting drug use and overdoses by considering diverse data obtained from different sources, including sewage-based drug epidemiology, healthcare data, social networks data mining, and police data.
- Such analysis will help to formulate more effective policies and programs to combat fatal opioid overdoses
EXISTING SYSTEM
- Medicine Overdose is cancer arising from the cervix.
- It arises due to the abnormal Drugs and spreads to other parts of the body.
- We can use machine learning techniques to predict if a person as Medicine overdose crisis or not.
- Different factors such as age, Medicine details, habits etc can be used to predict Dosage.
- Although, several researchers have tried to address the situation by developing intelligent systems using supervised machine learning methods, till date limited studies have used unsupervised machine learning algorithms.
- The Existing system has implemented five unsupervised algorithms, K-Means Clustering, DB-Scan, I-Forest, and Autoencoder.
DISADVANTAGES
The disadvantages are
- Medicine Overdose is a major dosage disease associated with aging, hypertension, and diabetes, affecting people 60 and over. Its major cause is the malfunctioning of the kidney in disposing toxins from the blood.
- Detection is not possible at an earlier stage.
- In the existing system, practical use of various collected data is time-consuming.
- Low Accuracy
PROPOSED SYSTEM
- The proposed system acts as a decision support system and will prove to be an aid for the physicians with the diagnosis.
- The algorithm ,Fuzzy c means uses clustering and makes use of clusters and data points to predict the relativity of an attribute.
- Each data point is associated with multiple clusters depending upon the membership degrees.
- The training data is trained by using proposed machine learning algorithm RCNN classification clustering and Adaboost feature extraction algorithm.
ADVANTAGES
- High performance and accuracy rate.
- RCNN Classification is very flexible and is widely in various domains with high rates of success.
PROJECT VIDEO
Software Requirements:
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
Hardware Requirements
- •Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
Including Packages
=======================
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
Specialization =======================
- * 24/7 Support * Ticketing System
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support
Opioid Overdose prediction, Opioid Overdose prediction using machine learning, drug overdose prediction, Drug overdose prediction using machine learning, Drug overdose prediction using machine learning github reddit, drug overdose prevention program, data-driven prediction of drug effects and interactions, drug prediction machine learning github, opioid overdose prevention, opioid overdose prevention training, drug overdose presentation, Data science projects GitHub for Beginners, Data Science projects github Python, Machine learning projects for final year, 2023 2024