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Heart Disease Detection
Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases.
Plant Disease Identification
In this project, convolutional neural network models are developed to perform plant disease detection through deep learning methodologies. Training of the models was performed with the use of an open database from plant village website that consists of a set of 38 distinct classes of [plant, disease] combinations, including healthy plants. Our proposed project includes various phases of implementation namely dataset importing, feature extraction, training the classifier and classification The deep learning models used would be VGG, INCEPTION, ALEXNET, AND RESNET
Chronic Kidney Detection
Predictive analytics for healthcare using machine learning is a challenged task to help doctors decide the exact treatments for saving lives. In this project, we present machine learning techniques for predicting the chronic kidney disease using clinical data. Four machine learning methods are explored including K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and decision tree classifiers. These predictive models are constructed from chronic kidney disease dataset and the performance of these models are compared together in order to select the best classifier for predicting the chronic kidney disease.