Early Detection and Diagnosis of Chronic Kidney and Breast Cancer Using Multi-level Machine Learning: A Hybrid Prediction Model
Keywords:
Chronic kidney disease (CKD), Breast cancer, Multi-level machine learning, Hybrid prediction model, ， Early detection and diagnosisAbstract
In this study, a multilevel machine learning approach is proposed for the early detection and diagnosis of chronic kidney disease (CKD) and breast cancer. The proposed hybrid prediction model uses a combination of supervised and unsupervised machine learning techniques, including Long Short-Term Memory (LSTM) and random forest algorithms, to improve the early detection and diagnosis of these diseases. The model also includes a feature selection process to extract the most relevant features from the data. The performance of the proposed model was evaluated on a dataset of patient information and compared with other machine learning models and traditional diagnostic methods. The results show that the proposed model outperforms traditional diagnostic methods and other machine learning models in terms of accuracy, sensitivity, and specificity in the early detection and diagnosis of CKD and breast cancer. The proposed multilevel machine learning approach provides an effective way to improve the early detection and diagnosis of CKD and breast cancer and has the potential to be used in clinical practice to improve patient outcomes.
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