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Various well-known health diseases affect millions of people worldwide. In the early stage, the clinicians may not recognize various clinical symptoms due to lack of reflection or anything else matter. So, such diseases are not easier to identify, and there may have chances to grow these illnesses and affect millions of people worldwide. Whenever an accurate early prediction is possible, the risk factor of such diseases severity can be lessened. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL …