The digital healthcare data is growing fast and this has created new chances for using data analytics and machine learning to make better medical decisions. Finding diseases early is very important to reduce the number of deaths lower the cost of treatment and improve the health of patients.. The old ways of diagnosing diseases often need people to analyze data manually and sometimes they fail to find diseases early because the symptoms are similar and the health conditions are complex. This research paper is about a system that uses data to predict diseases. This system uses healthcare data that has information about the patients, their medical history, symptoms and the results of their tests. The system first cleans up the data then it analyzes the data to understand it and finally it uses machine learning to find patterns and predict the likelihood of diseases early. The system tries out predictive models like Logistic Regression, Decision Tree, Random Forest and Support Vector Machine. It checks how well these models work by looking at things like how accurate they're how precise they are how well they remember things and their F1-score. The results show that machine learning models can really help analyze healthcare data and assist doctors in making decisions. The system shows how important data analytics is in making healthcare better and in supporting early diagnosis of digital healthcare data. The use of healthcare data in this system is a key factor, in its success.
Early Disease Prediction, Healthcare Analytics, Machine Learning, Data Mining, Predictive Modeling, Exploratory Data Analysis, Healthcare Datasets, Data Analytics
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