AI-Powered Healthcare Diagnostics: Innovations in Personalized Medicine

Abstract

Advances in AI are transforming healthcare diagnostics, enabling early detection and personalized treatment plans. This paper explores AI-powered solutions for healthcare applications, focusing on the integration of machine learning models into diagnostic workflows. Fatty liver disease is widely spread in the current era as it occurs based on the population factor as it gets increasing drastically. This disease spreads vastly and it results in sickness and death. It needs some early identification and diagnosis so that the patients can able to take some necessary measures to go through some earlier treatment, diagnosis, etc. So, here improper data analysis and diagnosis leads to some critical problem is considered. Here, a novel ML-based Random Forest approach is proposed in order to predict fatty liver disease as it classifies the risk factor of the individual patient to perform prevention of disease, early diagnosis, etc. The framework combines predictive analytics, image-based diagnostics, and patient-specific data to improve accuracy and efficiency. Case studies on fatty liver is identified based on the images of ultrasound and it helps to identify the disease and prediction uses certain variable of this disease to perform effective identification, decision making, etc. through this learning model. Demonstrate enhanced diagnostic capabilities and improved patient outcomes. The research highlights the potential for AI to revolutionize healthcare, particularly in personalized medicine. The effectiveness of this approach is analysed based on prediction accuracy, data specificity, data sensitivity with positive and negative value. The proposed model is compared with some existing models such as, e logistic regression (LR), Random Forest (RF), artificial neural networks (ANNs) and k-nearest neighbors (KNNs).