Machine Learning Approach for Early Disease Prediction and Risk Analysis
Keywords:
Precision in Disease Prediction, AI, MLAbstract
The optimistic future of AI integration in healthcare has become more imaginable in recent years because to AI's rapid development and the steady launch of AI research in the medical profession. The most significant promise for machine learning has been in applications like predicting how well a given drug will work. Clinicians have significant challenges when manually diagnosing abnormalities; this study aims to detect and predict people suffering from many diseases. Over time, both the accuracy and clarity of the pharmaceutical illness forecast have increased from the initial logistic regression to the machine learning prototype. This article takes a look at the many different machine learning frameworks available, as well as several common diseases and a brief explanation of the machine learning prediction methods used for each. Find the flaws in the current illness projection and estimate its growth going forward. Its overarching goal is to demonstrate ML's utility for disease prediction and to highlight the vital connection between ML and emerging medical technologies. The various feature extraction strategies available inside machine learning technologies may maintain their relevance in the field of medical study in the years to come.
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