Data Mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review

To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.

Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general medical population, those patients that require a referral to medical consultants and specialists. In many medical domains, including cardiovascular medicine, no gold standard exists for selecting referral patients. Where evidential selection is required using patient data, heuristics backed up by poorly adapted more general risk prediction models are pressed into action, with less than perfect results. In this study, existing clinical risk prediction models are examined and matched to the patient data to which they may be applied using classification and data mining techniques, such as neural nets. Novel risk prediction models are derived using unsupervised cluster analysis algorithms. All existing and derived models are verified as to their usefulness in medical decision support on the basis of their effectiveness on patient data from two UK sites.

In this survey, we collect the related information that demonstrate the importance of data mining in healthcare. As the amount of collected health data is increasing significantly every day, it is believed that a strong analysis tool that is capable of handling and analyzing large health data is essential. Analyzing the health datasets gathered by electronic health record (EHR) systems, insurance claims, health surveys, and other sources, using data mining techniques is very complex and is faced with very specific challenges, including data quality and privacy issues. However, the applications of data mining in healthcare, advantages of data mining techniques over traditional methods, special characteristics of health data, and new health condition mysteries have made data mining very necessary for health data analysis.

This review article provides an overview of the current state of data mining applications in healthcare, including case studies, challenges, and future directions. The article begins with a discussion of the role of data mining in healthcare, highlighting its potential to transform healthcare delivery and research. It then provides a comprehensive review of the various data mining techniques and tools that are commonly used in healthcare, including predictive modelling, clustering, and association rule mining. The article also discusses some key challenges associated with data mining in healthcare, such as data quality, privacy, and security, and suggests possible solutions. Finally, the article concludes with a discussion of the future directions of data mining in healthcare, highlighting the need for continued research and development in this field. The article emphasises the importance of collaboration between healthcare providers, data scientists, and policymakers to ensure that data mining is used ethically and effectively to improve patient outcomes and support evidence-based decision-making in healthcare.

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Analyzing the effects of various environmental factors on human diseases is one of the important issues in recent bioinformatics studies. In this paper we investigate several environmental factors regarding Type-2 diabetes and select some of them for develop an analytical model of disease risk prediction. For the selection of significant factors, we first preprocessed all the environmental factors into categorical values and then calculated the max/min odds ratios of all the categorized environmental factors. After that, we chose the top-n ranked factors as input features for the prediction model. The disease risk prediction model was developed with SVM classifiers, where training data were built based on Ansan/Ansung Cohort 2 Data obtained from the Korean National Institute of Health (KNIH). Here the data imbalanced problem was occurred in training data, which can be often observed in reality. Thus, to handle this problem, we regenerated the training data by using the SMOTE approach and used them for disease risk prediction modeling. For model evaluation, the proposed method was employed to predict the risk of Type-2 diabetes disease. The experiment results showed that our SVM classifiers based on selective environmental factors could produce very comparable results to the prediction model with genetic factors in forecasting the risk of specific disease.

Medical datasets have attracted the research community for possible analysis and suitable prediction, which helps the human to take proper precautions in preventing future diseases. To perform related operations, data mining techniques have been widely used in developing decision support systems for disease prediction through a set of medical datasets. This work proposes a new predictive model for disease prediction using pre-processing techniques for various disease datasets. The proposed model not only analyses the datasets also improves the performance by using ensemble methods. To process the datasets, pre-processing techniques such as discretization, resampling, principal component, and decision tree have been used. To classify the datasets, classification techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) have been used. The algorithms are applied with 10 fold validation technique. A predictive analysis has also been performed on various disease datasets, where every dataset results in significant improvement for various performance measures. We perform a predictive analysis on the datasets such as CKD (Chronic Kidney Disease), Cardiovascular Disease (CVD) or heart, Diabetes, Hepatitis disease, Cancer disease and ILPD (Indian Liver Patient disease). Experimental results show that the proposed predictive model outperforms in terms of better accuracy.

By Using data mining techniques, much study has been undertaken to derive information from medical data, which may enhance the curing ability. This study has been compared four automated methods for blood diseases detection using a different method for attributes reduction methods: PCA (Principal Component Analysis), ICA (Independent Component Analysis), original data set, and four algorithms, which are Naive Bayes, K-Nearest Neighbor, Decision Tree, and SVM (Support Vector Machine) for classification. The results show that after applying the PCA technique, the accuracy is: 0.98, 1.0, 0.79, and 0.78 for Naive Bayes, Decision Tree, SVM, and KNN and after applying the ICA technique, the accuracy is 0.76,.76, 0.76, and 0.74 for Naive Bayes, Decision Tree, SVM, and KNN, while the accuracy after applying on the original data set is: 0.98, 1.0, 0.78, and 0.75 for Naive Bayes, Decision Tree, SVM, and KNN. The results appeared that when applying decision tree on the original data set and after applying the PCA method, have been gotten the highest accuracy which is 100 %.

With the advent of the era of big data, machine learning theory is more and more widely used in disease prediction. At the same time, the use of disease risk and cost prediction model (DRCPM) (See Chapter 2 for details) to predict the chronic disease prevalence rates and medical expenses of patients has been recognized in the industry. In this paper, on the basis of DRCPM, In order to improve the prediction accuracy of the model. GA algorithm is used to optimize the super parameters and prediction weights of XGBoost and RF, and to construct GA_XGBoost_RF prediction model. Through experiments, the average prediction accuracy is 99.74% and the prediction variance is 1.7% on 1 million data sets, which is an extremely effective prediction method.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cross-sectional study was conducted. From January to December 2022, 158 patients underwent transnasal sellar pituitary tumor resection in three tertiary hospitals in Sichuan Province were selected as the research objects. The olfactory status was evaluated one week after surgery. They were randomly divided into a training set and a test set according to the ratio of 8:2. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. Based on different machine learning algorithms, BP neural network, logistic regression, decision tree, support vector machine, random forest, LightGBM, XGBoost, and AdaBoost were established to construct olfactory dysfunction risk prediction models. The accuracy, precision, recall, F1 score, and area under the ROC curve (AUC) were used to evaluate the model's prediction performance, the optimal prediction model algorithm was selected, and the model was verified in the test set of patients. Of the 158 patients, 116 (73.42%) had postoperative olfactory dysfunction. After missing value processing and feature screening, an essential order of influencing factors of olfactory dysfunction was obtained. Among them, the duration of operation, gender, type of pituitary tumor, pituitary tumor apoplexy, nasal adhesion, age, cerebrospinal fluid leakage, blood scar formation, and smoking history became the risk factors of olfactory dysfunction, which were the key indicators of the construction of the model. Among them, the random forest model had the highest AUC of 0.846, and the accuracy, precision, recall, and F1 score were 0.750, 0.870, 0.947, and 0.833, respectively. Compared with the BP neural network, logistic regression, decision tree, support vector machine, LightGBM, XGBoost, and AdaBoost, the random forest model has more advantages in predicting olfactory dysfunction in patients after transnasal sellar pituitary tumor resection, which is helpful for early identification and intervention of high-risk clinical population, and has good clinical application prospects.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Development of a coronary heart disease risk prediction model for type 1 diabetes: The Pittsburgh CHD in Type 1 Diabetes Risk Model

<p>In data mining, we are extracting data from historical knowledge and create data sets. Data mining is similar to discover data knowledge from data warehouse and converts it into understandable format. In India, furthersocieties are listening aboutthe medical fitness and health opinion problems. In China, more than 200 thousand people die due to medication errors and 100 thousand in USA .More than 45% medication errors are caused by physicians as specialists write the prescription giving to their practices which are quite limited. In India, at every moment to take care of health and maintain medicine records is very tedious task in pharmaceutical industry, medical ministry and health industry</p> <p><a href="https://journalnx.com/journal-article/20150678">https://journalnx.com/journal-article/20150678</a></p>

Background/Introduction Accurate cardiovascular disease (CVD) risk prediction models can facilitate the identification and cost-effective treatment of high-risk individuals. Discrimination and calibration are the most commonly recommended metrics for evaluating model performance. However, both are population-level measures, and it remains unclear whether these metrics are sufficiently sensitive to assess the performance of models designed for use with individual patients. Purpose Using a real-world dataset, we aimed to compare CVD risk estimates for example individuals (individual-level measures) and discrimination and calibration metrics (population-level measures) from two CVD risk prediction models; one with "age" as the only predictor and one with multiple predictors. Methods We constructed a cohort of almost all New Zealanders without CVD or heart failure, alive and aged 30–79 years in 2014, with follow-up linkage to hospitalisations and mortality until 2018 (N = 2,098,359). We derived two sets of sex-specific Cox regression models to predict 5-year CVD risk. The age-only models included "age" as the only predictor, and the full models used nine pre-defined routinely available CVD risk predictors with interaction terms. We calculated and compared individuals’ 5-year CVD risk from the age-only and full models and assessed model performance using Harrell’s C-statistics and calibration plots. Results Risk estimates for individuals using the two models showed substantial differences. A 65-year-old woman had predicted 5-year risks of 4.9% with the age-only model but 16.1% with the full model; a 74-year-old man had predicted risks of 15.2% and 9.2%, respectively, with age-only and full models (Figure 1). However, the models had similar discrimination and calibration (Figure 2). Age-only models had good discrimination with C-statistics of 0.773 (95% CI: 0.771–0.775) in women and 0.740 (0.738–0.742) in men, and calibrated well in both sexes. The full models had slightly better discrimination (C-statistics: 0.814 [0.812–0.816] in women and 0.769 [0.767–0.771] in men), and very similar calibration to the age-only models in both sexes. Conclusions Age-only models showed good discrimination and were well calibrated, but predicted very different risks in individual case studies compared to models with multiple predictors. This study calls into question the use of discrimination and calibration metrics as the main measures for comparing the performance of different CVD risk prediction models. We recommend that examples of predicted risk for individuals and an assessment of the magnitude and prevalence of cardiovascular predictors should also be considered when comparing the performance of prognostic models.