• 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.


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

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