International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Prediction of Diabetes and HyperTension using Machine Learning Techniques Lokesh Naik1, M Sakshi Rao2, Nagendra Prasad B G3, Navneeth C4, Mr. Navile Nageshwara Naveen5 1,2,3,4Students, Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India
Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India ---------------------------------------------------------------------***--------------------------------------------------------------------5Assisstant Professor,
Abstract - Diabetes, the worldwide health hazard, is
individuals. The use of wearable health monitoring devices as well as real-time data analytics can contribute to the accuracy of the estimates. It detects the presence of Hypertension which is one of the factors of detecting diabetic in individuals. It also seeks to search on the right food that can be taken by individuals (fruits and vegetables) in order to reduce the prevalence of diabetes among the people.
distinguished by persistant blood sugar abnormality which precipitates acute complication if it's not diagnosed and treated. Prevalence of this disorder underlines imperative to have effective forecast tools so as to find candidates at risk to adopt measures against prevention. It considers critical variables like Age, BMI, Hyper Tension and Heart Disease while predicting the disorder. Also, this project is intended in forecasting hypertension and heart disease. We implement drill through approach by identifying presence or absence of hypertension, one of the most important parameters in identifying diabetics. Work is all about early forecasting of diabetics and one of its parameter. This forecasting system help healthcare professionals target resources to high-risk patients and enhance preventive care outcomes. Future developments will involve incorporating real-time health data and broadening to encompass genetic and environmental influences for further optimization of predictive accuracy and scalability. Key Words: Regression
1.1 Motivation Diabetics is one of the critical health issues that appears to be common among people. Traditional diagnosis of sugar level will assess the current condition of the person. Developing Developing machine learning model to detect diabetics aims to predict at early stages. The model takes into account the other key factors such as hypertension, BMI and age. Following drill through approach, it also aims in detecting hypertension as well. This initiative leverages in predicting diabetics and foods (fruits and vegetables) to reduce glucose levels.
Diabetes, HyperTension, BMI, Logistic
1.2 Objective
1.INTRODUCTION
The goal is to provide early detection of diabetics and future prediction as per key parameters such as Hypertension, BMI and age. While the traditional diagnosis assess the current health condition, Machine learning approach aims in predicting it in early days as well as possibility in future. Finally, detecting diabetics and one of its parameter hypertension, it leverages in timely assessing and analysing the health conditions upon certain variation.
Diabetes is a critical illness that affects millions of people all over the world and challenges the health of the world population. It is seen as high sugar levels that emanate from lack of insulin, ineffective insulin as well as a combination of both. The traditional mechanisms used in testing for the disease such as fasting blood glucose tests as well as hemoglobin HBA1c tests offer information on sugar levels of individuals but may fail to detect the disease in its earliest stages in individuals suspected to be at risk. One of the best ways through which the risk of the disease can be determined is through the use of machine learning techniques that have the potential of understanding information on the levels of blood sugar that might not be easily detected through the use of traditional statistical approaches. It is through the use of these techniques that it is possible to train models through the use of large datasets and detect the high sugar levels as well as risk factors such as age, body mass index, physical activity as well as the presence of hypertension, thus making it easy to carry out a risk assessment of
© 2025, IRJET
|
Impact Factor value: 8.315
1.3 Scope The scope of this initiative is to detect and analyse diabetics and its parameter hypertension. Providing additional prediction for heart failure aims in assessing the major health issues that the world faces. Predicting diabetics and other health issues by considering parameters such as BMI, hypertension, smoking habits and age, analysis the effect of these attributes on health condition. The scope of this work also includes suggesting the appropriate in take of fruits and vegetables in the diet to reduce their glucose levels.
|
ISO 9001:2008 Certified Journal
|
Page 64