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In today's world, a massive amount of data is available in almost everysector. This data has become an asset as we can use this enormous amount ofdata to find information. Mainly health care industry contains many dataconsisting of patient and disease-related information. By using the machinelearning technique, we can look for hidden data patterns to predict variousdiseases. Recently CVDs, or cardiovascular disease, have become a leading causeof death around the world. The number of death due to CVDs is frightening. Thatis why many researchers are trying their best to design a predictive model thatcan save many lives using the data mining model. In this research, some fusionmodels have been constructed to diagnose CVDs along with its severity. Machinelearning(ML) algorithms like artificial neural network, SVM, logisticregression, decision tree, random forest, and AdaBoost have been applied to theheart disease dataset to predict disease. Randomoversampler was implementedbecause of the class imbalance in multiclass classification. To improve theperformance of classification, a weighted score fusion approach was taken. Atfirst, the models were trained. After training, two algorithms' decision wascombined using a weighted sum rule. A total of three fusion models have beendeveloped from the six ML algorithms. The results were promising in theperformance parameter. The proposed approach has been experimented withdifferent test training ratios for binary and multiclass classificationproblems, and for both of them, the fusion models performed well. The highestaccuracy for multiclass classification was found as 75%, and it was 95% forbinary. The code can be found in :https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction

The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach