International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Classifying and Predictive Analytics for Disease Detection: Empowering Healthcare Decisions with Convolutional Neural Network Dr. Aziz Makandar1, Miss. Nayan Jadhav2 1Professor, Department of Computer Science Karnataka State Akkamahadevi Women’s University. Vijayapura,
Karnataka, India
2Research Scholar, Department of Computer Science Karnataka State Akkamahadevi Women’s University.
Vijayapura, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The goal of this paper is to use techniques from
due to the increasing use of AI, CNN and ML. This presents a need for proactive measures to ensure that AI, CNN and ML are integrated into healthcare in a way that maximizes benefits while minimizing negative impacts. This paper focuses on the application of machine learning in healthcare, particularly in the early detection of diseases such as cancer, pneumonia, and tuberculosis. The project aims to develop a user-friendly web application that uses machine learning algorithms to provide predictive analytics for these conditions. The paper discusses the challenges and opportunities associated with this approach, highlighting the potential impact of AI, CNN and ML on the job market. Through this work, we hope to contribute to the ongoing discussion on the role of AI, CNN and ML in healthcare, with a particular focus on the challenges and opportunities of using these technologies for predictive analytics in disease detection.
machine learning and convolutional neural network to do predictive analytics on a lot of data from the healthcare industry. The goal is to use a lot of data to help doctors and nurses make better decisions about how to treat patients and how to care for their health. Breast cancer and other cancerrelated diseases kill a lot of people around the world, mostly because people don't get checkups on time and there aren't enough hospitals or doctors. India has only one doctor for every 1,456 people. The WHO recommends that there be one doctor for every 1,000 people. When these diseases are found and treated early, the results can be much better and even save lives. Using convolutional neural network and machine learning classification algorithms, the paper aims to predict dangerous diseases like pneumonia, skin cancer, brain tumors, lung cancer, tuberculosis, and breast cancer. A machine learning-based web application for medical tests has been made so that the predictions can be used by the general public. The goal of the paper is to make a user-friendly web app that uses machine learning and convolutional neural network to predict these diseases.
1.1 Problem statement The growing number of deaths around the world from diseases like breast cancer and other cancer-related diseases shows that healthcare needs to be better and timelier. Lack of medical infrastructure and a low ratio of doctors to patients make this problem worse and make it even more important to find and diagnose these diseases early. Machine learning techniques could be used to do predictive analytics on a lot of data in the healthcare industry. This would help doctors and nurses make better decisions about how to treat and care for their patients. But making a solution that is easy for the general public to use remains a challenge.
Key Words: Healthcare, machine learning, predictive analytics, early disease detection, cancer, pneumonia, tuberculosis, web application, medical tests, userfriendly, convolutional neural network (CNN), Artificial intelligence (AI).
1.INTRODUCTION Artificial intelligence (AI), convolutional neural network (CNN) and machine learning (ML) are revolutionizing the way we approach various aspects of our lives, including healthcare. Predictive analytics powered by machine learning and Convolutional neural network is providing new opportunities for early detection of diseases like cancer, tuberculosis, and pneumonia, enabling healthcare professionals to provide timely and effective treatments. This can lead to improved patient outcomes, reduced costs, and better allocation of medical resources. However, the use of AI, CNN and ML in healthcare also brings with it new challenges and opportunities. One of the most significant concerns is the potential impact of automation on the job market, with the fear that many jobs may become obsolete
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The goal of this paper is to solve this problem by making a web application that can predict different diseases: pneumonia, skin cancer, brain tumor, lung cancer, tuberculosis, and breast cancer. This application will use convolutional neural network and machine learning algorithms to predict these diseases. The goal is to make these predictions available to and usable by the general public through an easy-to-use medical test web app. This could help people find and diagnose these diseases earlier and save lives.
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