Comparative analysis of K-Means, SVM, Decision Tree and Naive Bayes in Predicting Diabetes Presence
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2023
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| _version_ | 1866901250106195968 |
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| author | Rohan Almeida Rajeev Dessai Ayush Noorani Amogh Pai Raiturkar Samuel Godinho |
| author_facet | Rohan Almeida Rajeev Dessai Ayush Noorani Amogh Pai Raiturkar Samuel Godinho |
| contents | In the context of rapidly growing amounts of data present today it is imperative to quickly dig out information from this data as information determines a large portion of the decision making process. Data mining allows us to do exactly that. Data mining is the process of turning raw data into useful information as it is an analytical process that allows us to find patterns, anomalies or correlations within large data sets that helps us acquire knowledge to predict outcomes or to validate findings. In order to find these patterns there are several algorithms. This paper discusses the following algorithms. 1) Decision Tree 2) Support Vector Machines 3) KNN 4) Naive Bayes The aforementioned algorithms are described as classification algorithms, it provides an interesting starting point for the analysis of them. The purpose of this paper is to analyze each of these algorithms to compare their prediction accuracy and features. We will be using diabetes prediction as the basis of this analysis and the Pima Indians Diabetes data set from kaggle.com as our input to determine whether a patient is diabetic or not. This paper will also highlight various other shortcomings or benefits of each algorithm and in which situations each of these algorithms perform the best. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18398040 |
| institution | Zenodo |
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| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Comparative analysis of K-Means, SVM, Decision Tree and Naive Bayes in Predicting Diabetes Presence Rohan Almeida Rajeev Dessai Ayush Noorani Amogh Pai Raiturkar Samuel Godinho Data Mining Comparative analysis Classification algorithms Decision Tree Support Vector Machines KNN Naive Bayes In the context of rapidly growing amounts of data present today it is imperative to quickly dig out information from this data as information determines a large portion of the decision making process. Data mining allows us to do exactly that. Data mining is the process of turning raw data into useful information as it is an analytical process that allows us to find patterns, anomalies or correlations within large data sets that helps us acquire knowledge to predict outcomes or to validate findings. In order to find these patterns there are several algorithms. This paper discusses the following algorithms. 1) Decision Tree 2) Support Vector Machines 3) KNN 4) Naive Bayes The aforementioned algorithms are described as classification algorithms, it provides an interesting starting point for the analysis of them. The purpose of this paper is to analyze each of these algorithms to compare their prediction accuracy and features. We will be using diabetes prediction as the basis of this analysis and the Pima Indians Diabetes data set from kaggle.com as our input to determine whether a patient is diabetic or not. This paper will also highlight various other shortcomings or benefits of each algorithm and in which situations each of these algorithms perform the best. |
| title | Comparative analysis of K-Means, SVM, Decision Tree and Naive Bayes in Predicting Diabetes Presence |
| topic | Data Mining Comparative analysis Classification algorithms Decision Tree Support Vector Machines KNN Naive Bayes |
| url | https://doi.org/10.5281/zenodo.18398040 |