- 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
- 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.
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
- 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.
- High performance and accuracy rate.
- RCNN Classification is very flexible and is widely in various domains with high rates of success.
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- •Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
- * 24/7 Support * Ticketing System
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support
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