Prediction of Dengue Disease through Data Mining by using Modified Apriori Algorithm

Jahangir, Iqra; Abdul-Basit,; Hannan, Abdul; Javed, Sameen

doi:10.1145/3213187.3287612

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…Thus, for this example, the following association rules are developed: S5→(S2,S1); (S1,S5)→S2 and (S2,S5)→S1. It has been noticed that the apriori algorithm of association rule mining has already been successfully deployed for prediction/diagnosis of heart diseases (Said et al, 2015;Domadiya & Rao, 2018;Jamsheela, 2021), dengue (Jahangir et al, 2018), brain tumor (Sengupta et al, 2013), chronic kidney disease (Alaiad et al, 2020), infectious diseases (Brossette et al, 1998), pandemic diseases (Burvin & Dhanalakshmi, 2018;Aiswarya et al, 2020), COVID-19 (Çelik, 2020;Shawkat et al, 2021;Tandan et al, 2021), pediatric primary care (Downs & Wallace, 2000), treatment of patients in an emergency department (Sarıyer & Taşar, 2020) etc. In this paper, based on a huge dataset of COVID-19 patients and using the FP growth algorithm of association rule mining, an attempt is put forward to discover COVID-19 symptom patterns and rules which would support the initial identification of severe COVID-19 cases for early treatment and isolation.…”

Section: Association Rule Miningmentioning

confidence: 99%

Association rule mining for prediction of COVID-19

Kumar

Chakraborty

2023

Decis. Mak. Appl. Manag. Eng.

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COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. The catastrophic shock of COVID-19 in India is also enormous. Currently, India has the largest number of COVID cases in Asia. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Based on a large clinical dataset, a linear regression model is also proposed having an accuracy of 73.9% in correctly predicting the occurrence of COVID-19.

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Section: Association Rule Miningmentioning

confidence: 99%

Association rule mining for prediction of COVID-19

Kumar

Chakraborty

2023

Decis. Mak. Appl. Manag. Eng.

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“…Berdasarkan pengamatan penelitian terdahulu yang menerapkan metode association rule yaitu "Prediction of Dengue Disease Through Data Mining By Using Modified Apriori Algorithm". Penelitian ini bertujuan untuk membantu dalam menganalisa faktor-faktor penyebab penyakit dengue dengan hasil nilai minimum support sebesar 60% dan nilai minimum confidence sebesar 90% [7]. Algoritma apriori digunakan karena algoritma ini merupakan Teknik asosiasi yang sederhana untuk menentukan pola penyebab penyakit dengue yang dialami penderita.…”

Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

Penerapan Algoritma Apriori Dalam Menentukan Pola Perilaku Dan Gaya Hidup Terhadap Penderita Hipertensi

Putri

Budianita

Syafria

et al. 2022

JNKTI

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Abstrak - Data mining merupakan teknik menggali informasi baru dari gudang data, informasi sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada bidang kesehatan terkhusus yang diteliti penulis yaitu penderita hipertensi. Hipertensi merupakan penyakit tidak menular yang prevalensinya sebesar 22% pada kelompok usia 18 tahun pada 2014 dan terus meningkat serta mampu meningkatkan risiko penyakit jantung koroner sebesar 12% dan meningkatkan risiko stroke sebesar 24%. Kebanyakan gejala yang dialami penderita tidak dapat dideteksi secara dini. Karenanya, perlu dilakukan penelitian dalam mendiagnosa pola perilaku dan gaya hidup terhadap penderita hipertensi menggunakan metode algoritma apriori. Data yang didapatkan melalui penyebaran kuisioner di puskesmas Melur dan rumah sakit Aulia Hospital. Atribut yang digunakan pada penelitian ini adalah jenis kelamin, usia, kebiasaan merokok, kebiasaan mengkonsumsi alkohol, intensitas aktifitas fisik, olahraga, dan pola konsumsi makanan. Pada pengujian parameter algoritma apriori dalam mencari pola dengan melihat hasil nilai support dan confidence pada metode algoritma apriori. Pengujian penelitian ini menggunakan tools Tanagra versi 1.4. Dari pengujian 300 data penderita hipertensi menggunakan nilai support 30% dan confidence 85% ditemukan 6 pola/rules dengan lift ratio ≥1.Kata kunci: Hipertensi, Diagnosa, Algoritma apriori, support, confidence, lift ratio Abstract - Data mining is a technique to dig new information from the data warehouse, information is very important and valuable because by mastering information, it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in the field of health, especially those studied by the author, namely people with hypertension. Hypertension is a non-communicable disease whose prevalence was 22% in the age group of ≥ 18 years in 2014 and continues to increase and is able to increase the risk of coronary heart disease by 12% and increase the risk of stroke by 24%. Most of the symptoms experienced by sufferers cannot be detected early.Therefore, it is necessary to conduct research in diagnosing behavioral patterns and lifestyles for hypertension patients using the a priori algorithm method. The data obtained through the distribution of questionnaires at the Melur health center and Aulia Hospital. The attributes used in this study were gender, age, smoking habits, alcohol consumption habits, intensity of physical activity, exercise, and food consumption patterns. In testing the parameters of the a priori algorithm, it is produced in looking for patterns by looking at the results of support and confidence values in the a priori algorithm method. Testing this study using Tanagra tools version 1.4. From testing 300 data on hypertension patients using support values of 30% and confidence of 85% found 6 patterns / rules with an lift ratio of ≥1.Keywords: Hypertension, Diagnosis, Apriori algorithm, support, confidence, lift ratio

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“… Study Objectives Models Applied Best Model [22] Predicting influenza outbreaks in Iran SVM, RF, ANN SVM (MAE = 0.132) [43] Detecting Disease Outbreaks among Physiological Variables FL FL ( F 1 Score = 0.820) [44] Predicting outbreak of hand-foot-mouth diseases RR, k -NN, RF, LSTM LSTM ⁎ (ROC = 0.841) [45] Predicting death and cardiovascular diseases in dialysis patients. LR, k -NN, CART, NB, SVC-RBF SVC-RBF (ACC = 0.953) [46] Event detection and Situational Awareness of disease outbreaks NB, SVM, LSTM LSTM ⁎ ( F 1 Score = 0.939) [47] Modelling disease outbreak events CRF CRF ( F 1 Score = 0.885) [48] Infection detection using physiological and social data in social environments k NN k NN (ROC = 0.798) [49] Detection and prevention of mosquito-borne diseases NB, RDT, J48, F k NN F k NN (ACC = 0.959) [51] Detecting the occurrence of Zika BPNN, GBM, RF BPNN ⁎ (ROC = 0.966) [53] Influenza Detection and Surveillance NB, ME, DLM NB (ACC = 0.700) [54] Detection on Dengue Diseases MAA MAA (ACC = 0.750) [55] Detection of Meningitis Outbreaks in Nigeria RF, ANN, k NN, LR, SVM NN ⁎ (ACC = 0.951) [56] ...…”

Section: Contents Summarizationmentioning

confidence: 99%

“…These types of evaluation measures depend on the problem type: regression or classification. For instance, the Mean Absolute Error (MAE) [13] , [18] , [22] , Mean Absolute Percentage Error (MAPE) [17] , [19] , [23] , [26] , [38] , [39] , [42] , Root-Mean-Square Error (RMSE) [25] , [28] , [30] , [31] , [32] , [33] , [40] and Mean Squared Error (MSE) [15] , [20] , [20] , [27] , [41] evaluation measures are used to solve the regression problems and Accuracy [22] , [49] , [53] , [54] , [55] , [56] , F 1 Score [43] , [46] , [47] , AUC-ROC [44] , [48] , [51] evaluation measures are used to solve the classification problems [67] . The performance comparison of various approaches and metrics are discussed in more detail in Section 6.4 .…”

Section: Contents Summarizationmentioning

confidence: 99%

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

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Prediction of Dengue Disease through Data Mining by using Modified Apriori Algorithm

Cited by 8 publications

References 1 publication

Association rule mining for prediction of COVID-19

Association rule mining for prediction of COVID-19

Penerapan Algoritma Apriori Dalam Menentukan Pola Perilaku Dan Gaya Hidup Terhadap Penderita Hipertensi

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

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