An Explainable Machine Learning Approach for Early Disease Prediction using Healthcare Data

Abstract

Finding diseases early is very important today. This is because illnesses such as heart disease, diabetes, and cancer can become very serious if they are not found on time. By the way, many people ignore small signs in their body, and that is why some diseases get worse before treatment starts. Because of this, we need smart systems that can warn us about health problems early. Modern healthcare is now using technologies like machine learning and data analysis to help with this. In this way, computers can study a lot of health information, such as age, blood pressure, sugar levels, and daily habits, to find patterns that humans may not notice easily. In this study, we try to make a system that predicts diseases early. First, data is collected from hospitals, medical records, and other sources. Then it is cleaned to remove mistakes or missing information, because good data helps the system give better results. A simple machine learning method, like Naive Bayes, is used to teach the system so that it can predict the chances of a person getting a disease. This helps doctors make decisions faster, so patients can get treatment sooner and healthcare costs can be lower. By the way, devices like smartwatches and mobile health apps can also give real-time information, such as heart rate and activity, to make predictions more accurate. These systems are made to help doctors, not replace them. In conclusion, using machine learning and data analysis for early disease prediction can save lives, improve treatment results, and make healthcare faster, better, and more efficient.