Risk Factors Analysis and Death Prediction in Some Life-Threatening Ailments Using Chi-Square Case-Based Reasoning (χ2 CBR) Model

Abstract

A wealth of data are available within the health care system, however, effective analysis tools for exploring the hidden patterns in these datasets are lacking. To alleviate this limitation, this paper proposes a simple but promising hybrid predictive model by suitably combining the Chi-square distance measurement with case-based reasoning technique. The study presents the realization of an automated risk calculator and death prediction in some life-threatening ailments using Chi-square case-based reasoning (χ CBR) model. The proposed predictive engine is capable of reducing runtime and speeds up execution process through the use of critical χ distribution value. This work also showcases the development of a novel feature selection method referred to as frequent item based rule (FIBR) method. This FIBR method is used for selecting the best feature for the proposed χ CBR model at the preprocessing stage of the predictive procedures. The implementation of the proposed risk calculator is achieved through the use of an in-house developed PHP program experimented with XAMP/Apache HTTP server as hosting server. The process of data acquisition and case-based development is implemented using the MySQL application. Performance comparison between our system, the NBY, the ED-KNN, the ANN, the SVM, the Random Forest and the traditional CBR techniques shows that the quality of predictions produced by our system outperformed the baseline methods studied. The result of our experiment shows that the precision rate and predictive quality of our system in most cases are equal to or greater than 70%. Our result also shows that the proposed system executes faster than the baseline methods studied. Therefore, the proposed risk calculator is capable of providing useful, consistent, faster, accurate and efficient risk level prediction to both the patients and the physicians at any time, online and on a real-time basis.

Modification of Case-Based Reasoning Similarity Formula to Enhance the Performance of Smart System in Handling the Complaints of in vitro Fertilization Program Patients

“…The low accuracy value of CBR (19.59%) is similar to that of 17.32% reported in previous research [ 25 ]. The difference in the accuracy value between the CCBR similarity formula and the CBR similarity formula of 32.99% also corresponds to reported in previous research of 35%, with CBR showing lower accuracy [ 26 ].…”

“…The difference in the accuracy value between the CCBR similarity formula and the CBR similarity formula of 32.99% also corresponds to reported in previous research of 35%, with CBR showing lower accuracy [26].…”

Intelligent Citizenship Identity Through Family Pedigree Using Graph-Signature Based Random-Forest Model

“…According to different scholars in machine learning field, there are different algorithms that can be used to approach different problems, these includes Bayesian, Decision tree, KNN, K-Means, SVM, ANN, SOM, Random forest etc. (Singh and Das, 2007;Hssina, Merbouna, Ezzikouri and Erritali, 2014;Adeniyi, Wei and Yongquan, 2015;Adeniyi, Wei and Yang 2018). A number of these algorithms have been studied; some show weakness in handling large data sets, inefficient rules when tested on new datasets, scalability problem, slow response, inability to handle noisy data sets, inaccuracy and computational complexity.…”

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

“…Jiyong Ding et al applied the improved CBR algorithm to the predictions of project performance, then took Nanjing HF project as an example, and proved the innovation of the method [6]. D. A. Adeniyi et al proposed the Chi-square case-based reasoning model and applied it to the realization of an automated risk calculator and death prediction, and then proved the precision rate and predictive quality [1]. Chanvarasuth et al applied the CBR algorithm to investment decisions, solved the choice of optimal future investment, and proved the method was superior to the traditional method by experiment [3].…”