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After passing the 10th class, every student is eager to know which educational program will be the best for their higher education to match their career goal. Sometimes, they are very much confused to decide the best path for their higher education, and they need help to determine the best suitable academic program to develop their careers and achieve their goal. So, we introduce an effective recommendation system to forecast each student's best educational program for their career development. This proposed research is accomplished by utilizing machine learning (ML) approaches to forecast every student's best academic path based on their past academic performances and recommend them the best suitable academic program for their higher studies. Class 10th standard passing student data are supplied to this automated system, and a correlation-based feature selection approach is applied to extract the relevant features for each academic program. This study utilizes multiple ML algorithms to provide the best results and forecast each student's academic performance and select the best model based on their performance for each educational program. Hence, the best-selected model and related features are involved in the recommendation process to provide the best suitable academic path for achieving every student's career goals.

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. 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) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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