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All Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH)
binti Rahmat, Azizah
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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IMPLEMENTATION OF FUZZY INFERENCE SYSTEM (FIS) FOR CARDIOVASCULAR DISEASES PREDICTION Sumarlinda, Sri; binti Rahmat, Azizah; Awang Long, Zalizah binti; Lestari, Wiji
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3418

Abstract

Abstract- Cardiovascular diseases (CVDs) continue to be a leading cause of mortality worldwide. Early and accurate prediction of CVDs risk is crucial for effective prevention and management. This study presents the implementation of a Fuzzy Inference System (FIS) for predicting suseptibility cardiovascular diseases. The implementation of FIS for the prediction of cardiovascular disease is by determining the membership function for risk factors that influence the susceptibility of the disease. The FIS developed in this study integrates five risk factors, including age, systolic blood pressure, diastolic blood pressure, blood sugar and cholesterol and one output parameter CVDs prediction. The FIS method used Mamdani with 162 rules. Real-world patient data diagnosed with cardiovascular disease is used to train and validate the FIS. Validity testing produces 100% valid data. Testing is carried out using patient data. The method used to validate the results of the FIS implementation is by distributing questionnaires to several paramedics.. These findings provide insights into further refinements of CVD risk modeling and potential applications in clinical practice.
Improvement Of Prediction Model Using K-Nearest Neighbors (Knn) And K-Means In Medical Data Lestari, Wiji; Sumarlinda, Sri; Binti Rahmat, Azizah
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4175

Abstract

Improving the performance of a prediction model is very important in its implementation. This study aims to improve the performance of the K-Nearest Neighbors (KNN) classification model with the K-Means clustering algorithm. The dataset used is UCI global data with 300 data and 12 features. The dataset is divided into 200 training data and 100 testing data. The training data is then processed by clustering with K-Means. The cluster centroid from the clustering results will be calculated for its distance from the testing data and produce data classification. The results of the classification process show that the accuracy of the proposed model is 76.45% better when compared to the results of the KNN classification process, for k = 5 the accuracy is 63.37%, k = 10 the accuracy is 64.36% and k = 15 the accuracy is also 64.36%.
Co-Authors Awang Long, Zalizah binti Sumarlinda, Sri Wiji Lestari