Progressive Analysis and Predictions of Leukemia (Cancer) Patients on a Machine Learning Model- The APPOLLO Hospitals Accredited

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Kamel Alikhan Siddiqui, Kaneez Fatima, Ali Hasan Khan, Sibghatullah


This abstract describes a machine learning model for predicting the prognosis of cancer patients with leukemia. The model is based on analyzing patient data such as age, gender, diagnosis, lab results, treatment history, and prior hospitalizations. The model employs supervised learning techniques such as random forests and gradient boosting to generate predictions of survival probabilities. Additionally, the model uses feature engineering to identify important features and reduce noise in the data. The model is evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics. The results indicate that the model has good predictive accuracy and can be used to identify high-risk patients and guide clinical decisions. The goal of this project is to develop a machine learning model for the diagnosis and prognosis of leukemia in Indian patients. The model will use a progressive analysis of patient data to identify the characteristics associated with leukemia, including genetic markers, environmental exposures, lifestyle factors, and demographic information. Using this information, the model will be used to accurately predict the risk of developing leukemia in Indian patients. Additionally, the model will be used to identify the most effective treatments for those diagnosed with leukemia and to monitor the disease progression. Finally, the model will be evaluated for its accuracy and effectiveness in a clinical setting.

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