Student’s academic performance or achievement has from time to time been a subject of discourse to academicians, scholars, researchers and educational institutions all over the globe. To this regard, schools are expected to play major and active roles in ensuring that students actually have good performance at end of their programmes. The academic performance is normally used to classify or predict how students would be ultimately capable to withstand and face future challenges after graduation. Students’ academic performance/achievement in any course of study plays a vital role in contributing and producing outstanding students who will be future viable leaders. The use of algorithms to classify and predict students’ academic performance/achievement is not new in machine learning using different techniques like neural network, logistic regression, decision tree and many more. This study classifies and predicts with the use of graphical technique called Decision Tree. The dataset was built from student’ attendance, practical assessment, assignment, ability to complete a free related course on internet, test score, and examination grade; the dataset was divided into training test and testing set. The training test was used to build and validate the decision tree algorithm (CHAID) while testing set was used to evaluate CHAID on the overall accuracy, sensitivity, and specificity. The results show that decision tree algorithm makes classification and prediction visible and clear with the use of graphics to display the results. Hence, the model built produces 96% accuracy.
There is indisputable proof that stress, anxiety, and depression significantly and negatively impact people's well-being. Recently, problems with stress and sadness have frequently resulted in a variety of chronic health concerns or even mortality. It is important to remember that stress, anxiety, and depression are all dangerous and closely associated. According to a proverb, "Life is 10% what you experience and 90% how you respond to it." This suggests that how we react to and equally manage whatever happens to us depends on how we respond to it. Several unknowns make the condition more ambiguous, such as diverse symptoms and different underlying causes of health disorders. Fuzzy can benefit medical professionals, experts, hospitals, drugs, etc. by handling the ambiguity and uncertainty of such vast amounts of data on people in these circumstances. To solve so many ambiguities, gaps in knowledge, or imprecision, fuzzy logic is frequently used. The current experiment applies a fuzzy method with fuzzy logic in R to develop a fuzzy inference system for pattern identification and classification to increase performance. This focuses on creating a fuzzy rule foundation, model, and inference for the study of data related to stress, anxiety, and depression. The results show that using a fuzzy inference system for uncertainties aided in making decisions that could have resulted in more serious problems if not handled on time. This study should only be used to observe the symptoms and causes of stress, anxiety, and depression; it should not be used to treat the identified health problems. Hospitals are the best places to solve problems.
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