Abstract

Millions of people die from cancer annually. Advanced metastatic cancers may not respond to traditional therapy. The importance for early diagnosis is highlighted by the difficulty of treating cancers in later stages. Enhancing patient outcomes using tissue-engineered cancer diagnosis and therapy is gaining popularity. Cancer and associated immune problems burden healthcare systems, making efficient, high-throughput drug development strategies essential. Thus, implanted chips may solve these issues. A revolutionary technique for early liver cancer identification is the Machine Learning-based Liver Cancer Diagnosis System (ML-LCDS). K-Nearest Neighbour (KNN) identifies liver tumors precisely in ML-LCDS. The performance evaluation reports sensitivity = 97.2%, specificity = 91.3%, precision = 93.5%, FPR = 8.7%, and accuracy = 94.1%, computed from the confusion matrix derived through 10-fold cross-validation. Experimental findings validate its consistent performance, establishing ML-LCDS as an efficient and reliable diagnostic tool for early-stage liver cancer detection.

Keywords

Implantable Chip, Advanced Diagnosis System, Healthcare Innovation, Early-Stage Liver Cancer Detection, Machine Learning-Based Liver Cancer Diagnosis System (ML-LCDS), K-Nearest Neighbour (KNN) Algorithm,

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