Authors: Rutika Bhagat, Prof. Pragati Patil
DOI Link: https://doi.org/10.22214/ijraset.2023.54228
Certificate: View Certificate
One of the most crucial aspects of someone\'s capacity to progress in life is their physical and mental well-being. Given the resources and requirements of society, the health-care system seeks to improve the populace as effectively as feasible. Due to a lack of timely medical equipment and treatments, death rates are growing in most nations. These health concerns can be prevented by offering standard healthcare services. The Flask framework was used to create the web application that houses our health monitoring system. In this Health Monitoring System, we employed Decision Tree Classification (Supervised Machine Learning method) to precisely anticipate outcomes. We used our own dataset to train and test our model. We could anticipate the patient\'s health level and area of risk based on that evaluation.
I. INTRODUCTION
Proper health monitoring is the main problem of today. Patients experience severe health issues as a result of inadequate health monitoring systems. Today, a patient's health can be tracked online by a wide variety of gadgets. These tools are being fully utilised by medical practitioners to keep track of their patients' health. With the emergence of hundreds of new healthcare technology startups, machine learning is transforming the healthcare sector. In this study, we will develop a health monitoring system that keeps track of the patient's BMI, age, gender, body temperature, blood pressure, pulse rate, alcohol use, and smoking habits. This approach can assist people in managing a healthy lifestyle by providing early risk projections and suitable personalised advice. We propose to (a) identify health risk factors, (b) conduct data collection from controlled trials, (c) perform data analyses, and (d) perform a predictive analysis with machine learning algorithms for future health risk predictions and behavioural interventions in order to develop a system that is intelligent, automated, personalised, contextual, and behavioural recommendations to achieve personal wellness goals. This system employs the decision tree classification method, which contributes to high accuracy and reliable patient health risk level prediction.
The main issue today is proper health monitoring. Patients experience major health-related problems as a result of inadequate health monitoring systems. There are numerous tools available today for online patient health monitoring. Health professionals are fully utilising these tools to monitor the wellbeing of their patients. Machine learning is transforming the healthcare sector with the emergence of hundreds of new healthcare technology firms. We'll create a health monitoring system in this article that keeps track of the patient's BMI, age, gender, body temperature, blood pressure, pulse rate, if they drink alcohol, and whether they smoke. With accurate customised suggestions and early risk projections, this system can help people manage a healthy lifestyle.
II. LITERATURE REVIEW
III. EXISTING SYSTEM
IV. PROPOSED SYSTEM
The main issue today is proper health monitoring. Patients experience major health-related problems as a result of inadequate health monitoring systems. There are numerous tools available today for online patient health monitoring. Health professionals are fully utilising these tools to monitor the wellbeing of their patients. Machine learning is transforming the healthcare sector with the emergence of hundreds of new healthcare technology firms. We'll create a health monitoring system in this article that keeps track of the patient's BMI, age, gender, body temperature, blood pressure, pulse rate, if they drink alcohol, and whether they smoke. With accurate customised suggestions and early risk projections, this system can help people manage a healthy lifestyle.
We propose to (a) identify health risk factors, (b) conduct data collection from controlled trials, (c) perform data analyses, and (d) perform a predictive analysis with machine learning algorithms for future health risk predictions and behavioural interventions in order to develop a system that is intelligent, automated, personalised, contextual, and behavioural recommendations to achieve personal wellness goals. This system employs the decision tree classification method, which contributes to high accuracy and reliable patient health risk level prediction.
The literature evaluation of heart disease prediction systems and overviews of current approaches are shown in the research article, which allows us to refine our method. In our method, we analysed various machine learning classification methods to manually and on the web platform predict the heart illness of each patient using the heart patients dataset from Alim et al. (2020).
The investigation reveals that Decision Tree and Random Forest approaches are 99% more accurate than other algorithms. Between these approaches, Decision Tree (0.0121) has a less classification error than Random Forest (0.0146). The goal of this research's further expansion is to use more advanced machine learning approaches to diagnose heart problems with 100% accuracy. By creating an Android app, research will be expanded upon for bettering user accessibility.
VIII. ADVANTAGES
IX. DIS-ADVANTAGES
X. APPLICATION
Applications of health monitoring utilising machine learning include early identification of cardiovascular diseases and chronic diseases, as well as Clinical Decision Support System (CDSS) that can help doctors, nurses, patients, and other carers in making better decisions. By contacting neighbouring hospitals, regular people can use this system to find out if they are suffering from a significant health issue and get care. With hundreds of new healthcare technology startups, machine learning is changing how the healthcare sector operates.
Instead of seeking treatment after a diagnosis, this research offers a way to prevent the condition through early intervention. With the help of the suggested system, healthcare workers can make better decisions, spot trends and innovations, and increase the effectiveness of research and clinical trials. It is also feasible to anticipate diseases more accurately. It enhances the way healthcare services are delivered, reduces costs, and carefully manages patient data.
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Copyright © 2023 Rutika Bhagat, Prof. Pragati Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET54228
Publish Date : 2023-06-18
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here