The sheer volume of health information we're generating today, and the fact that so many widespread and infectious diseases are out there, has really made it critical that we have smart tools that can help us figure out what's going on with our health. You know, the traditional ways of diagnosing diseases can take an eternity, cost a lot of money, and even lead to mistakes, particularly when doctors are trying to sort through a lot of possibilities at the same time. So, this research is all about creating a machine learning model – a smart assistant, basically – that can review a patient's basic health information and history and predict how likely they are to get a variety of diseases. The aim is to give healthcare professionals an early warning so that they can head things off and prepare the best response. We implemented a few of the various ways that these smart assistants learn in this project. We trained models using techniques such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (or SVM for short) to forecast things like diabetes, heart conditions, and Parkinson's. To make sure these models were working well, we cleaned the data used first. This meant choosing the most important information, making sure everything was working on the same level, and killing in the gaps. We then trained the models with measurements such as accuracy, precision, recall, and the F1-score to see which learned best. We also contrasted how each model performed differently for different illnesses.
Machine learning, Multi-Disease Prediction, Healthcare data, clinical data.
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