Predictive Angiogenesis-Cancer-Artificial Intelligence (PA-C-AI): Advancing Precision Medicine through Machine Learning for Personalized Treatment

S. Ariffa Begum^1*, J. Raja², N. Srinivas³, R. Nagendran⁴, G. Umamaheswari⁵, Srithar S⁶

PA-C-AI enhances angiogenesis prediction, enabling precise cancer therapies and personalized treatments, improving patient outcomes and survival rates.

Abstract

Background: Angiogenesis, the formation of new blood vessels, plays a critical role in cancer progression and other pathological conditions such as cardiovascular diseases and diabetic retinopathy. Predicting angiogenic patterns with high precision is vital for personalizing treatment and improving patient outcomes. The Predictive Angiogenesis-Cancer-Artificial Intelligence (PA-C-AI) framework combines artificial intelligence and machine learning techniques to address limitations in traditional methods, such as dependency on human expertise and limited data scalability. Methods: The PA-C-AI system integrates advanced machine learning algorithms, including neural networks, to analyze complex biological datasets. Multi-omics data and real-time patient monitoring were incorporated to enhance the system's accuracy and flexibility. A diverse dataset of angiogenesis markers was used to train and validate the framework. Automated data analysis eliminated bias, ensuring consistent and reproducible results. Results: The PA-C-AI framework achieved 96.2% prediction accuracy in modeling angiogenesis, significantly surpassing traditional approaches. It effectively distinguished angiogenic mechanisms, including sprouting, intussusceptive angiogenesis, and vascular mimicry, addressing gaps in current research. Personalized treatment plans demonstrated a 97.2% success rate, with improved therapeutic outcomes reported at 99.3%. The automated processes achieved 95.38% efficiency in data interpretation. Conclusion: The PA-C-AI system represents a transformative step forward in precision medicine, offering enhanced prediction of angiogenesis, personalized treatment planning, and interdisciplinary collaboration. Its applications extend beyond cancer to other angiogenesis-dependent diseases, such as cardiovascular conditions and diabetic retinopathy. Future developments will focus on expanding prediction capabilities and validating the system's clinical significance to facilitate integration into healthcare systems for AI-enhanced, patient-specific therapies.

Keywords: Angiogenesis, Artificial Intelligence, Machine Learning, Personalized Medicine, Cancer Therapy

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