Diabetes Risk Prediction System using Machine Learning and React. The project performs clinical risk assessment based on health and lifestyle inputs. Features include data preprocessing, ML classification, and a React UI with age ranges, tooltips, and risk-based outputs.

Machine Learning project that predicts lifestyle-related health risks using data analysis and predictive modeling. Built an end-to-end ML pipeline including preprocessing, feature engineering, EDA, and models (regression, classification, clustering) using Python, Pandas, NumPy, and Scikit-learn.

Hair Fall Prediction System using Machine Learning that analyzes lifestyle, health, and genetic factors to predict hair fall risk, identify key causes, segment users into risk groups, and generate personalized recommendations.

Team Graduation Project: Mental Health & Depressive Disorder Risk Analysis & Prediction using Machine Learning on lifestyle and medical factors

LOGISTIC REGRESSION - HEART DISEASE PREDICTION Introduction World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients and in turn reduce the complications. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression Data Preparation Source The dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. Demographic: • Sex: male or female(Nominal) • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • Current Smoker: whether or not the patient is a current smoker (Nominal) • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10 year risk of coronary heart disease CHD (binary: “1”, means “Yes”, “0” means “No”) Logistic Regression Logistic regression is a type of regression analysis in statistics used for prediction of outcome of a categorical dependent variable from a set of predictor or independent variables. In logistic regression the dependent variable is always binary. Logistic regression is mainly used to for prediction and also calculating the probability of success. The results above show some of the attributes with P value higher than the preferred alpha(5%) and thereby showing low statistically significant relationship with the probability of heart disease. Backward elimination approach is used here to remove those attributes with highest P-value one at a time followed by running the regression repeatedly until all attributes have P Values less than 0.05. Feature Selection: Backward elimination (P-value approach) Logistic regression equation P=eβ0+β1X1/1+eβ0+β1X1P=eβ0+β1X1/1+eβ0+β1X1 When all features plugged in: logit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucoselogit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucose Interpreting the results: Odds Ratio, Confidence Intervals and P-values • This fitted model shows that, holding all other features constant, the odds of getting diagnosed with heart disease for males (sex_male = 1)over that of females (sex_male = 0) is exp(0.5815) = 1.788687. In terms of percent change, we can say that the odds for males are 78.8% higher than the odds for females. • The coefficient for age says that, holding all others constant, we will see 7% increase in the odds of getting diagnosed with CDH for a one year increase in age since exp(0.0655) = 1.067644. • Similarly , with every extra cigarette one smokes thers is a 2% increase in the odds of CDH. • For Total cholesterol level and glucose level there is no significant change. • There is a 1.7% increase in odds for every unit increase in systolic Blood Pressure. Model Evaluation - Statistics From the above statistics it is clear that the model is highly specific than sensitive. The negative values are predicted more accurately than the positives. Predicted probabilities of 0 (No Coronary Heart Disease) and 1 ( Coronary Heart Disease: Yes) for the test data with a default classification threshold of 0.5 lower the threshold Since the model is predicting Heart disease too many type II errors is not advisable. A False Negative ( ignoring the probability of disease when there actually is one) is more dangerous than a False Positive in this case. Hence in order to increase the sensitivity, threshold can be lowered. Conclusions • All attributes selected after the elimination process show P-values lower than 5% and thereby suggesting significant role in the Heart disease prediction. • Men seem to be more susceptible to heart disease than women. Increase in age, number of cigarettes smoked per day and systolic Blood Pressure also show increasing odds of having heart disease • Total cholesterol shows no significant change in the odds of CHD. This could be due to the presence of 'good cholesterol(HDL) in the total cholesterol reading. Glucose too causes a very negligible change in odds (0.2%) • The model predicted with 0.88 accuracy. The model is more specific than sensitive. Overall model could be improved with more data

Machine Learning is the fastest-growing technique in many fields and the healthcare industry is no exception to this. Machine Learning algorithms plays an essential role in predicting the presence/absence of Heart diseases, tumors, and more. Such required information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and treat the patient accordingly. World Health Organization has estimated 12 million deaths occur worldwide, every year due to heart diseases. Half the deaths in the United States and other developed countries are due to cardiovascular diseases. The early prognosis of stroke diseases can aid in making decisions on lifestyle changes in high-risk patients and in turn reduce the complications. If it is about to identify the relationship and factors affecting it can cured n advance time. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression. In this report, I'll discuss the prediction of stroke using Machine Learning algorithms. The algorithm I have implemented is logistic regression on the Health

This project aims to predict whether an individual is at risk of having a stroke based on various demographic, lifestyle, and health-related factors. The dataset used for this project is the Stroke Prediction Dataset from Kaggle.

Agentic AI-driven disease risk prediction from lifestyle data with personalized health insights and attention

🌱 Assess health risks in young adults using machine learning to analyze lifestyle habits and predict personalized health outcomes.

EduCarePCOS is an AI-powered risk assessment tool for Polycystic Ovary Syndrome (PCOS). By analyzing user-entered clinical and lifestyle data, it provides personalized risk predictions to support early detection. The tool also helps users understand thier risk factors, guides self-management, and promotes informed decisions regarding thier health.

Heart Disease Prediction App This app uses a trained Random Forest machine learning model to analyze various health parameters such as age, cholesterol, blood pressure, heart rate, diabetes status, lifestyle habits, and more. It predicts whether a person is at risk of heart disease and displays a visual risk score .

The Diabetes Prediction Model is an AI-powered tool designed to identify individuals at risk of developing diabetes by analyzing various health and lifestyle factors. By leveraging machine learning algorithms, the model aims to provide early detection and intervention strategies to help prevent or manage the onset of diabetes.

Machinery learning is a fast-expanding area that will change the method for the diagnosis and management of this chronic condition by applying itself to diabetes as a global pandemic. Machine learning principles have been used to build algorithms to help predictive models of the likelihood of diabetes development or related complications. Digital therapy has shown to be a well-established lifestyle care intervention for diabetes control. Patients are becoming more self-managed, and the assistance of therapeutic decision-making is available to both them and health care practitioners. Machine learning helps patient signs and bio-markers to persist, unburdened, remotely controlled. Social networking and online forums also increase patient commitment to the treatment of diabetes. Development in technologies helped to optimize the use of diabetes tools. These smart technological reforms together have led to an improved glycemic regulation, a decrease in fast glucose and glycosylated hemoglobin levels. Machine learning introduces a change in diabetes treatment model from traditional management techniques to data-driven care growth The trouble with medicines is that various drug formulations can cure the condition in several ways. As the diabetic population grows, new medications are increasingly emerging. In order to treat common diseases such as elevated cholesterol and high blood pressure, diabetics also continue to take other drugs. With the patient's age and other physical conditions, the potency of these medicines varies In this method, the effectiveness, risks of side effects and costs are measured side by side, and are readily grasped by doctors and patients. The most prevalent form of Type 2 diabetes effects more people as people grow up. This disease has also escalated dramatically due to the spread of western diets and lifestyles to developing countries. Diabetes is an incurable metabolic illness that happens when high blood sugar is present, and may have deadly effects. Today, medicine, nutritious diets and exercise will regulate diabetes. It is also unpredictable to choose and administer the most appropriate mixture of prescription, which is stable, cheap and well tolerated by patients as well By applying an adequate methodology for the design and development of systems experts can achieve objectives satisfactorily, as in the case of the Weiss and Kuligowski methodology. On the other hand, machine learning has several knowledge machine algorithms, which can be useful to be applied on various data sets through the different interfaces that offers, as the option of Explorer and Datasets, which were worked in this case of study, or to be included in other applications. Furthermore, both tools, contain what is necessary to conduct data transformations, grouping, regression, clustering, correlation and visualization tasks. Because they are designed as extensibility-oriented tools which allows to add new functionalities to a project, because it can be combined with other programming languages such as Prolog, for generation more robust expert systems Readmitted diabetes patients Machine learning techniques allow to automatically identify patterns and even make predictions based on a large amount of data that could be extracted from the computer systems used to ascertain information on readmission of diabetes patients. The analysis Clustering or grouping is a technique that allows exploring a setoff objects to determine if there are groups that can be significantly represented by certain characteristics, in this way, objects of the same group are very similar to each other and different from objects in other groups. The results obtained by comparing the relevance of different attributes as well as the use of two of the most popular algorithms in the world of machine learning are presented: neural networks and decision trees. Automatic classification of blood glucose measurements will allow specialists to prescribe a more accurate treatment based on the information obtained directly from the patients' glucometer (Hosseini et al, 2020). Thus, it contributes to the development of automatic decision support systems for gestational diabetes. This high level of glucose in the blood is transferred to the fetus causing various disorders: excessive growth of adipose tissues, which increases the need for caesarean section, neonatal hypoglycemia and increased risk of intrauterine fetal death (Dagliati et al, 2018). It also increases the risk of type 2 diabetes once the gestation period is over for both the mother and the fetus. The project proposes the development of intelligent and educational tools for the survey based on neurodiffuse techniques integrated into a telemedicine system. Telemedicine systems have been used with success on numerous occasions in diabetes and the integration of decision support tools in this type of system helps a better interpretation of the data (Abhari et al, 2019).

A Health-Risk Prediction and Healthy Lifestyle Advisor Application based on your Demographic jnfo and Medical History.

🧠 Risk Behavior Prediction using Demographic & Lifestyle Data | ML Classification Models + Power BI Dashboard | Targeting health interventions by analyzing risky habits like smoking & drinking.

🧠 Risk Behavior Prediction using Demographic & Lifestyle Data | ML Classification Models + Power BI Dashboard | Targeting health interventions by analyzing risky habits like smoking & drinking.

Diabetes Prediction among individ- uals using information from the 2015 BRFSS survey, such as self-reported health-related risk factors, demographic characteristics, and lifestyle behaviors.

Web-based ML (hardcoded scoring scheme) platform for inherited disease risk prediction: uses family history, lifestyle & health records to generate personalized prevention plans and screening recommendations.

MindSphere is a web application that assesses mental health risk based on lifestyle, demographic, and psychological factors. It prioritizes explainable AI (XAI), providing users with risk scores and visual explanations of predictions.

Our disease prediction system uses advanced data analytics to forecast individual health risks, leveraging factors like genetics, lifestyle, and medical history for personalized insights and proactive healthcare strategies.

This project aims to develop an AI-driven health insurance premium prediction model to accurately assess risk and pricing for individuals based on health metrics, lifestyle, and demographic factors. The goal is to reduce extreme prediction errors while maintaining business-aligned accuracy.

The Diabetes Prediction Model is a machine learning system that predicts the risk of diabetes using health data such as glucose level, BMI, blood pressure, insulin, and age. It helps in early detection, enabling timely treatment and lifestyle changes to prevent serious health complications.

Blood_Sugar_Detection_using_machine_Learning is a predictive analytics project that uses machine learning models to detect and classify blood sugar levels based on health and lifestyle data. It aims to assist in early diabetes detection and health risk prediction through smart, data-driven insights.

A state-of-the-art method to determine your risk of diabetes is available on our Diabetes Prediction website. We deliver customized risk assessments and useful insights by examining your health data, lifestyle, and family history. Gain knowledge to empower yourself and take proactive measures for a healthier future.

The NHANES dataset contains rich health, lifestyle, lab, and biomarker information from a large, diverse U.S. population. This project aims to build a reproducible pipeline that leverages NHANES data to develop and benchmark machine learning models for CVD risk prediction, with an interactive dashboard for exploring key risk factors.

Diabetes Prediction Using Machine Learning is a data-driven project aimed at predicting diabetes risk based on health metrics and lifestyle factors. Using machine learning models, the project classifies individuals as healthy, pre-diabetic, or diabetic, enabling early detection and preventive healthcare.

An AI-powered Gradio web app that predicts mental health risk levels based on lifestyle inputs like sleep, stress, screen time, and more. The app uses a trained machine learning model and provides downloadable logs and a visual report of user predictions.

A Streamlit web app that predicts lung cancer risk using a machine learning model trained on health and lifestyle data. Users input symptoms like smoking, coughing, chest pain, etc., and receive a prediction with probability. Built with Python, scikit-learn, and deployed using Streamlit Cloud.

Heart disease prediction refers to the application of statistical techniques and machine learning algorithms to predict the likelihood of a person developing heart-related issues based on historical health data. It is a crucial step in preventive healthcare, enabling early diagnosis, treatment planning, and lifestyle adjustments to reduce risk.

Over the last two decades, heart disease, also regarded as cardiovascular disease, is the major cause of death worldwide. Conferring to the World Health Organization, over 17.9 million individuals have died every year as a result of coronary heart disease, with coronary stroke responsible for 80% of all fatalities. Deaths in large numbers are frequent in low and middle-income countries. Heart disease is driven by a range of factors, including private and job-related practices, as well as inborn predisposition. Smoking, heavy alcohol and caffeine consumption, stress, and insufficient physical activity, as well as health sedentary lifestyles, hypertension, increased cholesterol levels, and pre-existing heart disorders, are all risk factors for heart disease. The capability to diagnose heart disease early and precisely plays a critical part in taking preventive steps to avoid fatalities. In certain cases, heart disease can be completely cured by a mixture of dietary changes, medicine, and if required surgery. With the proper treatment, the symptoms of heart failure can be reduced and the heart's rhythm can be improved. The estimated outcomes can be used to avoid and therefore reduce the cost of surgical care as well as other costs. My work's ultimate goal would be to reliably forecast using various attributes and come up with a solution that will assist them in avoiding potential losses. To come up with an accurate prediction and observations about the person's well-being, this prediction model will use a data science life cycle, along with machine learning models.