Genetic Feature Research Articles

A Critical Review of the Effectiveness of Machine Learning & Deep Learning Approaches in Forecasting Stock Market Trends

For a long time, traders as well as researchers have been actively interested in financial market forecasting. A financial market prediction has an interest for investors & researchers since long time. An existing method for predicting stock prices often rely on technical and fundamental analysis, which have limitations in handling the complexity and volume of financial data. To handle complex data sets for forecasting the value of stock using new emerging learning techniques. The most commonly used approach such as artificial neural networks (ANN), neural network techniques, SVM, decision trees algorithm, and random forests. These techniques can capture complex nonlinear relationships between variables and adjust to changing market conditions. The machine learning & deep learning analyze the trend for the future values of stock prediction and provides observation for making decision. The importance of feature selection and data preprocessing are used for improving the prediction accuracy of stock value. The genetic method feature selection concept can reduce the dimensionality of the data and remove irrelevant features, while data preprocessing techniques such as normalization and scaling can improve the stability and convergence of the algorithms. Sentiment analysis of social media data can capture market sentiment and investor behavior, while news articles can provide insights into company performance and industry trends. The accuracy of predictions tends to decrease as the prediction horizon increases and the prediction accuracy varies across different stock markets. A lot of significant work, the most recent stock market-related prediction system includes multiple limitations. Now the conclusion, the projection of stock markets is a hybrid approach & certain parameters for stock market forecasting should be as accurate as possible.

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system. The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively. This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

A forensic population database of autosomal STR and X-STR markers in the Qiang ethnic minority of China

The Qiang ethnic group is one of the oldest ethnic groups in China and is the most active ethnic group among all the populations along the Tibetan-Yi corridor. They have had a profound impact nationally and internationally. The paternal and maternal genetic feature of the Qiang ethnic group has been revealed, leaving the question of the genetic characteristics from autosomes and X chromosome not answered. The aim of this study was to explore the potential of 36 A-STR (Microreader™ 36A ID System) and 19 X-STR (Microreader™ 19X System) for application in the Qiang population and to elucidate their genetic diversity in southwest China. The cumulative probability of exclusion (CPE) for autosomal STRs is 1–1.3814 × 10−15 and the mean paternity exclusion chance (MEC) for X-STRs is 1–1.7323 × 10−6. Forensic parameters suggest that the STRs analyzed here are well-suited for forensic applications. The results of phylogenetic, interpopulation differentiation, and principal coordinates analysis (PCoA) indicate that the Qiang people have extensive connections with ethnic minorities in China, supporting the view that the Qiang people are the oldest group in the entire Sino-Tibetan language family. The Qiang appeared genetically more associated with most ethnic groups in China, especially the Han. The calculation of random matching probability (RMP) was improved by Fst correction of allele frequencies to make RMP more accurate and reasonable. This study can fill in the gaps in the Qiang STR reference database, providing valuable frequency data for forensic applications and evidence for the Qiang's genetic pattern as an important ancestral position in the Sino-Tibetan populations.

Variable Selection for Recurrent Events Using Heuristic Approaches: Identifying Informative Variables for Rehospitalization in Schizophrenia Patients

Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
 Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
 Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
 Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.

Imbalance-learning road crash assessment under reduced visibility settings: A proactive multicriteria decision-making system

Road crash prediction is a fundamental key in designing efficient intelligent transportation systems. There has been a pronounced progress in the use of machine learning models for crash events assessment by the transportation safety research community in recent years. However, little attention has been paid so far to evaluating reduced-visibility crash occurrences within a heuristic ensemble system. This study presents a proactive multicriteria decision-making system that can predict crash occurrences based on real-time roadway properties, land zones’ characteristics, vehicle telemetry, driver inputs and weather conditions collected using a desktop driving simulator. A key novelty of this work is implementing a genetic algorithm-based feature selection approach along with ensemble modeling strategies using AdaBoost, XGBoost and RF techniques to establish effective crash predictions. Furthermore, since crash events occur in rare instances tending to be underrepresented in the dataset, an imbalance-learning methodology to overcome the issue was adopted on the basis of several data resampling approaches to increase the predictive performance namely SMOTE, Borderline-SMOTE, SMOTE-Tomek Links and ADASYN strategies. To our knowledge, there has been a limited interest at adopting an ensemble-based imbalance-learning strategy examining the impact of real-time features’ combinations on the prediction of road crash events under reduced visibility settings.

An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach

The rapid proliferation of Internet of Things (IoT) devices has raised critical concerns regarding the security of corresponding IoT networks. The Routing Protocol for Low-Power and Lossy Networks (RPL), a foundational element in IoT communication, is susceptible to diverse routing attacks due to IoT nodes’ constrained resources and open nature. This underscores the necessity for an Intrusion Detection System (IDS) to safeguard RPL-based IoT networks. Existing anomaly-based IDS suffer from high false alarm rates (FAR). In response to these challenges, this paper presents the Ensemble Learning-based Intrusion Detection System (ELG-IDS), which employs stacking and extreme parameter optimization to detect three RPL internal attacks: version number, decreased rank, and DIS flooding attacks. ELG-IDS employs enhanced feature extraction and genetic algorithm (GA)-based feature selection. Experimental results on a dedicated dataset demonstrate ELG-IDS's remarkable accuracy: 99.18%, 99.38%, 99.66%, and 97.90% for version number, rank attack, DIS flooding, and an average accuracy of 97.90% in multi-classification mode, respectively. This study advances IoT network security through ELG-IDS, enhancing protection against evolving security challenges.

An integrated pipeline for prediction of Clostridioides difficile infection

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.

The Integrated Bioinformatic Assay of Genetic Expression Features and Analyses of Traditional Chinese Medicine Specific Constitution Reveal Metabolic Characteristics and Targets in Steatosis of Nonalcoholic Fatty Liver Disease.

In this study, our primary aim is to analyze the genetic expression feature and analyze specific Traditional Chinese medicine (TCM) constitution distribution in non-alcoholic fatty liver disease (NAFLD) and reveal the metabolic characteristic of NAFLD. For revealing genetic features, we obtained the gene expression data from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information(NCBI). The genetic data on NAFLD were analyzed by identifying differentially expressed genes (DEGs), associated pathways, co-expressed genetic networks, and gene set enrichment function. Concurrently, we assessed specific constitution distributions among local NAFLD patients through established TCM constitution models and determined the independent variable, including specific constitution to the NAFLD via the regression analyses. The analyses on GEO datasets showed that simple steatosis in NAFLD is strongly associated with HOMA-insulin resistance (HOMA-IR). Analyses of GEO datasets revealed significantly altered genetic expression profiles between NAFLD and normal populations. For TCM constitution analyses, we demonstrated a decline in yin-yang harmony (YYH) and yang-asthenia (YAAC) constitution, whereas there was an increase in qi-stagnation (QSC) and phlegm-dampness (PDC) in NAFLD. The binary logistic regression analysis indicated that besides other metabolic parameters, YYH, qi asthenia (QAC), YYAC, and yin-asthenia (YAC) were the independent variables of NAFLD, while YAC was the independent variables of T2D. The multilinear regression analyses suggested that NAFLD, DM, BMI, waist, TC, TG, hypertension, ALT, AST, and YAC were the significant determinators of the FPG. This study presents a relatively comprehensive metabolic profile in steatosis of NAFLD, revealed by significant genetic expression feature alterations and different TCM constitution distribution in NAFLD. Through this method, the study intends to associate the genetic feature with the phenotype of TCM constitution. The results could be applied to assist integrative medicine research in exploring the appropriate personalized approaches for NAFLD.

Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. We utilized a dataset comprising 2000 protein sequences, including 1000 SARS-CoV-2 sequences and 1000 non-SARS-CoV-2 sequences. Feature extraction provided 27 essential features representing the primary structural data, achieved through the Discere package. To optimize performance, we employed machine learning classification algorithms such as K-nearest neighbor (KNN), XGBoost, and Naïve Bayes, along with feature selection techniques like genetic algorithm (GA), LASSO, and support vector machine recursive feature elimination (SVM-RFE). The SVM-RFE+KNN model exhibited exceptional performance, achieving a classification accuracy of 99.30%, specificity of 99.52%, and sensitivity of 99.55%. These results demonstrate the model’s efficacy in accurately classifying coronavirus protein sequences. Our research successfully developed a robust classification model capable of early detection and prediction of protein sequences in SARS-CoV-2 and other coronaviruses. This advancement holds great promise in facilitating the development of targeted treatments and preventive strategies for combating future viral outbreaks.

Machine Learning Model For Stunting Prediction

This study aims to find the best Supervised Machine Learning (SML) model for stunting prediction. This research was conducted using an experimental approach using 192 infant data with a composition of 183 normal infant data and 9 stunted infant data using a custom dataset. The conclusion obtained from this study can be concluded that the combination of the Random Forest classification algorithm with Support Vector Machine Weighting and the Genetic Algorithm Feature Selection has the best performance. The parameters with the best performance are: The training and testing data distribution is 90% of the training data and 10% of the testing data. The number of trees in the random forest algorithm is 100, and the Gain Ratio criterion and max_depth is 10. In the Genetic Algorithm, the best parameters are: The Roulette Wheel selection method, the population is 20, the mutation value is 0.03, and the crossover value is 0.9. The validation method uses k-fold cross validation with a value of k = 10. Another conclusion is that there are 44 supporting factors for stunting, which, if we take a ranking of 10 in order of magnitude from largest to smallest, the supporting factors for stunting are 1.Baby's weight at birth. 2.Baby’s Height at Birth. 3.Number of meal per day. 4.Breast Milk. 5.Diarrhe times per 3 month. 6.Child development examination during covid by Health Worker at home. 7.Mother's age at birth. 8.Mother height at birth. 9.Number of sibling. 10.Age when the first food was given. This research has the disadvantage of no test on other datasets. So researchers do not know the reliability of findings is on different datasets

A Robust Cardiovascular Disease Predictor Based on Genetic Feature Selection and Ensemble Learning Classification

Timely detection of heart diseases is crucial for treating cardiac patients prior to the occurrence of any fatality. Automated early detection of these diseases is a necessity in areas where specialized doctors are limited. Deep learning methods provided with a decent set of heart disease data can be used to achieve this. This article proposes a robust heart disease prediction strategy using genetic algorithms and ensemble deep learning techniques. The efficiency of genetic algorithms is utilized to select more significant features from a high-dimensional dataset, combined with deep learning techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF), to achieve the goal. The boosting algorithm, Logit Boost, is made use of as a meta-learning classifier for predicting heart disease. The Cleveland heart disease dataset found in the UCI repository yields an overall accuracy of 99.66%, which is higher than many of the most efficient approaches now in existence.

Paediatric B lymphoblastic leukaemia with hyperdiploidy and a false-positive KMT2A fluorescence in situ hybridization result

The dramatic improvement in the event-free survival of paediatric B-lymphoblastic leukaemia (B-ALL) has led to risk-stratified treatment. Through a combination of clinical features, cytogenetic abnormalities and assessment of treatment response, patients are stratified to receive different intensities of therapy. The presence of high hyperdiploidy (>50 chromosomes) is considered a favourable genetic feature. Conversely, KMT2A fusion genes in B-ALL are associated with a poor prognosis, resulting in intensification of treatment. We present a seven-year-old female with B-ALL, a high hyperdiploid karyotype (56 chromosomes) and KMT2A rearrangement detected on FISH, but with no productive fusion identified. Single nucleotide polymorphism (SNP) array suggested the KMT2A rearrangement was due to chromosome 11 chromothripsis. Subsequent targeted RNA fusion panel and whole transcriptomic sequencing (mRNA-seq) did not detect an expressed KMT2A fusion. Differential expression analyses of the mRNA-seq data led to clustering of this case with other hyperdiploid cases, consistent with the hyperdiploid cytogenetic results. Given the additional intensity and potential toxicity of high-risk treatment, unusual findings by chromosome analysis, FISH and/or chromosomal microarray should prompt consideration of testing for a KMT2A fusion by another method to avoid misclassification.

Retracted: An Immune-Related Genetic Feature Depicted the Heterogeneous Nature of Lung Adenocarcinoma and Squamous Cell Carcinoma and Their Distinctive Predicted Drug Responses.

[This retracts the article DOI: 10.1155/2022/8447083.].

Classification of Electrocardiogram Signals using Deep Learning based on Genetic Algorithm Feature Extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.

Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking

Bankruptcy is an impact caused by a company's financial failure. Financial failure in the company must be avoided so as not to cause losses to the company. In the research that was carried out utilizing a data set from the Taiwan Economic Journal as many as 6,819 to be trained using machine learning algorithms using classification techniques. The goal obtained from the research conducted is to obtain a classification technique with the best accuracy results. The method used in this research is preprocessing using the synthetic minority over-sampling technique to handling unbalanced data sets. Then, the results of the balanced data set will be processed using a genetic algorithm-support vector machine feature selection algorithm to reduce the attributes of the data set. Data sets that have experienced reduced attributes will be trained using the stacking method with a single classifier base learner in the form of k-nearest neighbors, naïve bayes, decision trees with classification and regression tree models, gradient boosting decision trees, and light gradient boosting. The meta-learner used in the stacking method is extreme gradient boosting. The results of the accuracy obtained from the research conducted were 99.22%.

Phylogenetic and Genomic Characterization of Whole Genome Sequences of Ocular Herpes Simplex Virus Type1 Isolates Identifies Possible Virulence Determinants in Humans.

There are limited data on the prevalence and genetic diversity of herpes simplex virus type 1 (HSV-1) virulence genes in ocular isolates. Here, we sequenced 36 HSV-1 ocular isolates, collected by the Bascom Palmer Eye Institute, a university-based eye hospital, from three different ocular anatomical sites (conjunctiva, cornea, and eyelid) and carried out a genomic and phylogenetic analyses. The PacBio Sequel II long read platform was used for genome sequencing. Phylogenetic analysis and genomic analysis were performed to help better understand genetic variability among common virulence genes in ocular herpetic disease. A phylogenetic network generated using the genome sequences of the 36 Bascom Palmer ocular isolates, plus 174 additional strains showed that ocular isolates do not group together phylogenetically. Analysis of the thymidine kinase and DNA polymerase protein sequences from the Bascom Palmer isolates showed multiple novel single nucleotide polymorphisms, but only one, BP-K14 encoded a known thymidine kinase acyclovir resistance mutation. An analysis of the multiple sequence alignment comprising the 51 total ocular isolates versus 159 nonocular strains detected several possible single nucleotide polymorphisms in HSV-1 genes that were found significantly more often in the ocular isolates. These genes included UL6, gM, VP19c, VHS, gC, VP11/12, and gG. There does not seem to be a specific genetic feature of viruses causing ocular infection. The identification of novel and common recurrent polymorphisms may help to understand the drivers of herpetic pathogenicity and specific factors that may influence the virulence of ocular disease.

Correlation of t(14;18) translocation breakpoint site with clinical characteristics in follicular lymphoma.

t(14;18)(q32;q21) translocation is an important genetic feature of follicular lymphoma resulting in antiapoptotic B-cell lymphoma 2 (BCL2) protein overexpression. On chromosome 18 breakpoint-site variation is high but does not affect BCL2. Breakpoint most commonly occurs at major breakpoint region (MBR) but may happen at minor cluster region (mcr) and between MBR and mcr at 3'MBR and 5'mcr. The aim of this study was to analyze the correlation of t(14;18)(q32;q21) breakpoint site with clinical characteristics in follicular lymphoma. We included patients diagnosed with follicular lymphoma who received at least 1 cycle of systemic treatment and had the t(14;18)(q32;q21) translocation detected by polymerase chain reaction (PCR) at MBR, mcr or 3'MBR prior to first treatment. Among patients with different breakpoints, sex, age, disease grade, stage, B-symptoms, follicular lymphoma international prognostic index (FLIPI), presence of bulky disease, progression free survival and overall survival were compared. Of 84 patients, 63 had breakpoint at MBR, 17 at mcr and 4 at 3'MBR. At diagnosis, the MBR group had a significantly lower disease stage than the mcr group. Although not significant, in the MBR group we found a higher progression-free survival (PFS) and overall survival (OS), lower grade, age, FLIPI, and less B-symptoms. Compared to patients with mcr breakpoint, those with MBR breakpoint seem to be characterised by more favourable clinical characteristics. However, a larger study would be required to support our observation.

Fault diagnosis of Rolling Bearing for Motor Based on LSTM-EEMD and Genetic Optimization

In the process of ensemble empirical mode decomposition (EEMD) for motor rolling bearing time series, if the classifier is trained directly using the eigenvalues extracted from the pattern components, there are two shortcomings leading to the reduction of fault identification accuracy as follows: decomposition has serious endpoint effects; the correlation between extracted features lead to the confusion of the fault feature vector classification boundary. Aiming at the problems, in this paper, a fault diagnosis model was built. Firstly, LSTM is used to extend the original data to reduce the divergence degree of the endpoint. Secondly, correlation analysis and dimensionality reduction were carried out for the extracted features to reduce the feature dimension. Finally, the eigenvalues were weighted and genetically optimized to enhance the boundary of different eigenvectors. The experimental results validated by data sets showed that the accuracy of fault diagnosis would be improved by using LSTM extension and genetic optimization feature vector.

Genetic Algorithm-Wavelet Transform Feature Extraction for Data-Driven Acoustic Resonance Spectroscopy

Acoustic Resonance Spectroscopy enables highly accurate measurement of the properties (geometry/material) of a structure based on the structure's natural vibrational resonances. In general, measuring a specific property in multibody structures presents a significant challenge due to the complex overlapping peaks within the resonance spectrum. We present a technique for extracting useful features from a complex spectrum by isolating resonance peaks that are sensitive to the measured property and insensitive to other properties (noise peaks). We isolate specific peaks by selecting frequency regions of interest and performing wavelet transformation, where the frequency regions and wavelet scales are tuned via a Genetic Algorithm. This contrasts greatly from traditional wavelet transformation/decomposition techniques, which use a large number of wavelets at different scales to represent the signal, including the noise peaks, and results in a large feature size, thus decreasing Machine Learning generalizability. We provide a detailed description of the technique and demonstrate the feature extraction technique for example regression and classification problems. We observe reductions of 95% and 40% in regression and classification errors, respectively, when using the Genetic algorithm/Wavelet Transform feature extraction, compared to using no feature extraction, or using Wavelet Decomposition, which is common in optical spectroscopy. The feature extraction has potential to significantly increase the accuracy of spectroscopy measurements based on a wide range of Machine Learning techniques. This would have significant implications for ARS, as well as other data-driven methods for other types of spectroscopy, e.g. optical.

Comparative Analysis of Data Mining Techniques for Heart Disease Prediction: A Focus on Neural Networks and Decision Trees

Heart disease is a general term used to describe numerous medical conditions that directly affect the heart and its various components. It is a prevalent health concern in modern times. The focus of this paper is to evaluate different data mining techniques for the prediction of heart disease, which have been introduced in recent years. The findings indicate that neural networks using 15 attributes demonstrate the best performance among all other data mining techniques. Additionally, the analysis concludes that decision trees, with the assistance of genetic algorithms and feature subset selection, also exhibit high accuracy. The study concludes that data mining techniques can effectively predict heart disease and that the choice of technique depends on the specific context of the analysis. The study suggests that decision trees and artificial neural network models are suitable for heart disease prediction. The study also recommends further research to explore the use of other data mining techniques for heart disease prediction.