  In today's world, a massive amount of data is available in almost every
sector. This data has become an asset as we can use this enormous amount of
data to find information. Mainly health care industry contains many data
consisting of patient and disease-related information. By using the machine
learning technique, we can look for hidden data patterns to predict various
diseases. Recently CVDs, or cardiovascular disease, have become a leading cause
of death around the world. The number of death due to CVDs is frightening. That
is why many researchers are trying their best to design a predictive model that
can save many lives using the data mining model. In this research, some fusion
models have been constructed to diagnose CVDs along with its severity. Machine
learning(ML) algorithms like artificial neural network, SVM, logistic
regression, decision tree, random forest, and AdaBoost have been applied to the
heart disease dataset to predict disease. Randomoversampler was implemented
because of the class imbalance in multiclass classification. To improve the
performance of classification, a weighted score fusion approach was taken. At
first, the models were trained. After training, two algorithms' decision was
combined using a weighted sum rule. A total of three fusion models have been
developed from the six ML algorithms. The results were promising in the
performance parameter. The proposed approach has been experimented with
different test training ratios for binary and multiclass classification
problems, and for both of them, the fusion models performed well. The highest
accuracy for multiclass classification was found as 75%, and it was 95% for
binary. The code can be found in :
https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction
