As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.

Today’s world faces a serious public health problem with cancer. One type of cancer that begins in the breast and spreads to other body areas is breast cancer (BC). Breast cancer is one of the most prevalent cancers that claim the lives of women. It is also becoming clearer that most cases of breast cancer are already advanced when they are brought to the doctor’s attention by the patient. The patient may have the evident lesion removed, but the seeds have reached an advanced stage of development or the body’s ability to resist them has weakened considerably, rendering them ineffective. Although it is still much more common in more developed nations, it is also quickly spreading to less developed countries. The motivation behind this study is to use an ensemble method for the prediction of BC, as an ensemble model aims to automatically manage the strengths and weaknesses of each of its separate models, resulting in the best decision being made overall. The main objective of this paper is to predict and classify breast cancer using Adaboost ensemble techniques. The weighted entropy is computed for the target column. Taking each attribute’s weights results in the weighted entropy. Each class’s likelihood is represented by the weights. The amount of information gained increases with a decrease in entropy. Both individual and homogeneous ensemble classifiers, created by mixing Adaboost with different single classifiers, have been used in this work. In order to deal with the class imbalance issue as well as noise, the synthetic minority over-sampling technique (SMOTE) was used as part of the data mining pre-processing. The suggested approach uses a decision tree (DT) and naive Bayes (NB), with Adaboost ensemble techniques. The experimental findings shown 97.95% accuracy for prediction using the Adaboost-random forest classifier.

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Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

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