Exploring the Role of Artificial Intelligence in Personalized Healthcare: From Predictive Diagnostics to Tailored Treatment Plans

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Dr. Mohammad Ahmar Khan, Dr. Shanthi Kumaraguru, Dr. RVS Praveen, Narender Chinthamu, Dr Rashel Sarkar, Nilakshi Deka, Dr. Anurag Shrivastava

Abstract

 Currently technology particularly AI is transforming personalized healthcare through sharpening of the prediction of diseases and also creating improved care plans. As part of the work, four algorithms, namely Random Forest, SVM, CNN, and KNN are examined to enhance the delivery of health services. Incorporating the dataset from electronic health, genomic, and medical image, the study shows high improvements in diagnostic accuracy and targeted therapy. The accuracy score of the model was 0.869 when the model was the Random Forest. 5% and factors like age as well as the cholesterol levels are some of the features that play central role. The developed SVM model with the use of radial basis kernel provided an accuracy of 88. 3%, outperforming other kernels. CNNs applied in the medical image context made improvements to feature extraction with an F1-score of 0. 867. Thus, in relation to the classification aspect, KNN was found to classify chest X-ray images with an accuracy of 84 percent. 7 percent, with the remaining vote set aside to decide the patient category. The findings also demonstrate AI’s ability to provide targeted and specific healthcare services based on the advanced analysis of large datasets and the improvement of decision-making frameworks. The findings of this study highlight the importance of AI in the further progression of P4 medicine, and establishes the frameworks for this realm’s forward progress.

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