International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
Health1: A Disease Detection Tool Akshay Anil Baviskar1, Divya Pandey2, Alok Singh3, Pratyush Mahalle4, Suyash Varma5, Prof. Ashvini Jadhav6 1,2,3,4,5UG Scholar, Dept. Of Information Technology, MIT ADT University, Pune 6Professor, Dept. Of Information Technology, MIT ADT University, Pune
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Abstract - Artificial intelligence is radically changing
because the data is presented in huge amount and that some data mining and machine learning techniques are used. The integration of the trio: machine learning, Python, and Flask has, gratefully, established a new way in which many medical diseases are detected and managed In the paradigm shifting, ever-changing healthcare civilization. Our project’s vector, “Health Cure Solution”, is grounded on specific nonnegotiables centre on tirelessly nourishing accessible and accurateness available in healthcare, and providing it in suitable amount in our evolving health matter alignment, attributable to many factors that instantly renew the arena of the formation of modern creativeness for living and wellness of today. The platform’s health-focus is structured is seeded on the instant, convenient observation and detection of several crucial diseases, accumulated through a diversity grade of machine learning Platform, and the program language Python, and the robust useable interface in the Flask framework. Python is the platform’s dynamic language that is versatile with the extensive and coherent number of official and member released packages that plan on making autonomous Python functions downstream and can be effectively employed source files. Python packs containing NumPy, SciPy, and Pandas, this packs deliver a lucid foundation onto which extra engaged scientific software production uses reason without fear of exerting low volumes of analogies. Similarly, many discipline-applied packages are available for forecasting needs from the meteorological field. Also, State-of-the-art Machine Learning algorithms have also been implemented.
medical practice. Advanced levels in machine literacy, digital data collection, and calculating structure have permitted artificial intelligence operations to penetrate into fields believed not long ago to be concerned with the elite dispatch of mortal specialists. This review essay outlines the fiscal, legal, and social consequences of artificial intelligence when it comes to the setting of health care operations. Many diseases are happening to human beings due to the environment condition and living habit. So, predicting the disease in the early phase has become an important task. But detection using symptom is too difficult for doctors to get accurate output. So, disease detection should be the most difficult task. But by considering data mining, the problem of disease prediction can be solved. On the other hand, supervised machine learning algorithms showed great potential to outperform the current standard disease diagnosis process and medical professionals in the early detection of high risk diseases. The objective is to identify the patterns between various supervised ML models and performance metrics to identify disease detection trends. A limited number of advancements in AI technologies and their biomedical operations are also summarized as well as the obstacles to the far restraint of additional developments in AI for Health Cure Application. Some of the major Features of the Health Cure Application consists of: It can able to detect 7 type of diseases which are –: Covid-19 detection, brain Tumour detection, breast cancer detection, Alzheimer’s detection, diabetes detection, pneumonia detection and Heart disease detection.
An expected outcome of these project is predating the disease in advances, and it can prevent the threat of life in time and save people’s life and reduces the cost of treatment to some extent. Detective diseases are 1. Breast cancer detection 2. Diabetes detection. 3. Heart disease detection. Recent work on deep learning has focused on several largest areas in machine learning. Machine learning models will learn and understand after the raw data’s hierarchical representation with some preprocessing data and it has been declared as a concept called as big data technology and more attention towards disease prediction has been devoted.
Key Words: CNN, Machine Learning, XGBoost
1.INTRODUCTION Disease Inference System is the most relevant and efficient for machine learning used to predict human diseases is to provide the relevant symptoms. Our system uses powerful machine learning algorithms for symptoms from the user’s prediction of diseases. The health care system has used and produced quite a large amount of data that can be used to develop knowledge about a patient’s unique disease. The healthcare information will be previous treated for the treatment of the patient’s health with effective and best possible treatment. This area also needs some improvement using informative data in health science. However, the main problems are the extraction of information from that data
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1.1 Breast Cancer Detection Breast cancer is recognized as a multifactorial disease in the world and the most common cancer in women, with 30% of all cancer cases in women; 15 million women are
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