Health-Monitoring-system-by-using-Machine-Learning

It predict the result based on the condition of the patient like low, medium and high the features of ECG data, Temperature data, Presssure and Patient Id. Here, we use Five Modules to shown the result. Datasets Collection : With the help of iot the datasets are collected the features of ecg data, temperature data, pressure data and Patient id in the form of CSV file. Datasets PreProcessing : It is used to process the data into proper format of the system. Data Visualization : Here, we use the Exploratory data analysis(EDA) concept like bar chart, data correlation matrix, Histogram plot and Kernel distribution plot etc. Model Implementation : Here, we use the different types of machine learning algorithm like Logistic Regression, Naive Bayes, Decision Tree and Support Vector Machine are used to train and predict the result of the system. Classification and Prediction : All the algorithm machine learning model accuracy score are showed and then also shown the performance metrics like classification report of precision, recall, f1 score and support and then also shown the confusion matrix and then also shown the comparison graph of the different machine learning algorithm score and then finally user given the input data in the form of array and then final prediction shown the condition of the user like low, medium and high.