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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Informatics and Communication Technology (JICT)
Umi Atmaja, Dewi Marini
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics

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IDENTIFY CHOLESTEROL DISEASE RISK LEVELS USING MULTIPLE LINEAR REGRESSION ALGORITHMS Haryadi, Deny; Umi Atmaja, Dewi Marini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 8 No 1 (2022): JITK Issue August 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1474.806 KB) | DOI: 10.33480/jitk.v8i1.3328

Abstract

Cholesterol is one of the fat compounds found in the bloodstream that are necessary for the formation of several hormones and new cell walls in the liver. Normal cholesterol levels in the human body are in the range of < 200 mg / dL. If cholesterol levels in the blood are abnormal or excessive, it can result in dangerous diseases such as heart disease or stroke. In this study, cholesterol disease prediction will be carried out using models formed from linear regression methods, so that the results of this study can be used as a reference for early prevention of cholesterol disease and become a means of decision making. Linear regression is one of the prediction methods in data mining that can be used to find out how dependent variables/criteria can be predicted through independent variables or predictor variables individually. In this study by utilizing some data of patients with cholesterol disease that has been stored in the database using several attributes, namely age, BMI, glucose, and cholesterol. So by applying a linear regression algorithm can be done a prediction in the identification of cholesterol diseases based on functional relationships on the attributes in the data. The results of this study showed an RMSE value of 0.347 with a standard deviation of /- 0.000. This shows that the model resulting from linear regression algorithms with the above cases is quite accurate.
Prediction of Liver Disease Using a Linear Regression Algorithm Haryadi, Deny; Umi Atmaja, Dewi Marini; Hakim, Arif Rahman
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.182

Abstract

The liver is the most essential organ in the human body. Hepatitis is one such disorder affecting the liver and is a global health issue, including in Indonesia. Liver disease is an inflammatory condition of the liver that can be triggered by genetic factors, viral infections, alcohol consumption, and the use of certain drugs. In principle, prevention of hepatitis or liver disease can be done by adopting a healthy lifestyle. In addition, early detection is also very important in preventing death in those affected by this disease. One method for early detection is through the application of data mining, which can help predict and reduce mortality in patients affected by this disease. Linear regression is a data mining technique utilized to predict the dependent variable or outcome based on the independent variable or predictor. The study conducted tests on this algorithm and obtained a Root Mean Squared Error of 0.349 +/- 0.000. This indicates the presence of a correlation or functional relationship (cause and effect) between the dependent variable (criterion) and the independent variable (predictor). The purpose of this testing process is to detect liver disease using the linear regression algorithm.
Co-Authors Arif Rahman Hakim Deny Haryadi