The Future of Personalized Healthcare: AI and Machine Learning Approaches to Predicting Patient Response

Abstract

The purpose of individualized health care is to provide the patient with the specific healthcare care by being extremely precise when predicting the response of the patient. The present research examines the potential of real-world artificial intelligence (AI) and machine learning (ML) to enter into predicting the outcomes of patients more effectively. The other method makes use of state of the art ML-based models that can be used to make predictions based on volumes of data that can be fed in an AI form in regards to data on the nature and history of patients and genetic data. The paper will analyze the recent advancements regarding the sphere of AI and ML in order to elaborate individual care packages, as well as, predictive modeling of the clinical environment. The most crucial ones are the evaluation of the relevance and applicability of the existing models, one of which is the decision tree, the neural networks and a support vector machine, in analyzing the response of the patient. In the methodology of the research, it is possible to assume comparing various models of AI, and the purpose behind it is to disprove and test them; therefore, to design the solution, data sets of medical information published publicly should be utilized. On the basis of the findings, it is concluded that ML models, in particular, deep learning methods produce very good results and are vastly superior to all the conventional methods in terms of the accuracy level and consideration of the needs of each of the patients in question. Such consequences of the implications of the findings on the future practice of healthcare are considered at the conclusion of the end of the study where the statement of the need of inserting the transparent and interpretable models into the clinical decision-making process is formulated.