AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes
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AI‑based smart prediction of clinical disease using random forest classifier and Naive Bayes V. Jackins1 · S. Vimal1 · M. Kaliappan2 · Mi Young Lee3 Accepted: 20 October 2020 © The Author(s) 2020
Abstract Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the dataset used. Keywords Artificial intelligence · Diabetes · Data mining techniques · Naïve Bayes classification · Random forest classification
* Mi Young Lee [email protected] V. Jackins [email protected] S. Vimal [email protected] M. Kaliappan [email protected] 1
Department of IT, National Engineering College, Kovilpatti, Tamil Nadu, India
2
Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India
3
Department of Software, Sejong University, Seoul, South Korea
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V. Jackins et al.
1 Introduction Due to modern lifestyle, diseases are increasing rapidly. Our lifestyle and food habit leads to create impact on our health causing heart diseases and other health issues. Data mining technique is one of the most challenging and leading research areas in healthcare due to the high importance of valuable data [1]. The recent blooming in the data mining approaches has provided a solid platform for various applications in the healthcare field. In healthcare, data mining is playing a vital role in different fields like intrusion detection, pattern recognition, cheaper medical treatments’ availability for the patients, disease diagnosing and finding its procurement methods [2, 3]. An artificial intelligence makes the system more sensitive and activates the system to think. In machine learning, AI acts as a subfield to perform better prediction [4]. It also accommodates the researchers in the field of healthcare in development of effective policies, and different systems to prevent different types of disease, early detection of diseases can reduce the risk factor. The aim of our work is to predict the diseases among the trained dataset using classification algorithms. It has been trained the Naive Bayes and random forest classifier model with three different disease datasets namely—diabetes, coronary heart disease and cancer dat
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