Comparison of Cart and Naive Bayesian Algorithm Performance to Diagnose Diabetes Mellitus

Abstract

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.

LTAI Management Based on Blockchain Technology to Increase Alexa Rank

“…Based on LTAI research based on blockchain technology to increase alexa rank on the Alphabet Incubator web there are 3 (three) conclusions, including: Increasing activities on the Alphabet Incubator web through blockchain-based LTAI technology to increase alexa rank requires a moderator as a manager in socializing LTAI schedules so that maximum activity can occur on the Alphabet Incubator web. Increasing the Alphabet Incubator rank for LTAI based on blockchain technology requires activities on the Alphabet Incubator such as creating the LTAI schedule and the schedule function to greatly affect the Alphabet Incubator web rank value [27]. Increasing student interest can make LTAI schedule requests on the Alphabet Incubator web.…”

Authenticity of a Diploma Using the Blockchain Approach

“…Advantages and benefits of blockchain technology is applied in the field of education is the reduced level of fraud in the falsification of a certificate due to transparency in the blockchain where everyone who is connected to each other in a network has the same record, the record will be updated if all people who are connected to each other in the network agreed to the note. Thus, the records stored will be more accurate, and consistent when compared to without using blockchain technology [30]- [32]. And it will be more time-efficient and cost-efficient to prove the authenticity of a certificate [33]- [35].…”