Quadratic Discriminant Analysis Research Articles

A Feature Extraction Method for Prognostic Health Assessment of Gas Compressor Valves

Abstract This paper presents features derived from the pressure-volume (PV) diagram that is useful in estimating different valve faults in reciprocating compressors with a strong potential of remaining useful life prediction. The PV diagram is expected to deviate depending on valve wear conditions. The valve degradation types explored in this work are leakage, seat wear, and spring fatigue in the suction and discharge assemblies of a Dresser-Rand ESH-1 compressor commonly used in the petrochemical industry. The proposed method estimates the polytropic exponent on the compression and expansion phase as well as the discharge and suction valve loss power and uses them as features for a quadratic discriminant analysis. The features are created through in-cylinder pressure, suction pressure, discharge pressure, and crank angle measurements collected on a single-stage, dual-acting compressor operating on air with wear precisely machined and seeded into the poppets of the inlet and outlet valves. A very high classification accuracy is achieved in distinguishing the wear types, severity, and location with strong prognostic trends.

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

The identification and characterization of immunogenic tumor antigens are essential for cancer vaccine development. In light of the impracticality of isolating and evaluating each putative antigen individually, in silico prediction algorithms, particularly those utilizing machine learning (ML) approaches, play a pivotal role. These algorithms significantly reduce the experimental workload necessary for discovering vaccine candidates. In this study, we employed six supervised ML methods on a dataset comprising 212 experimentally validated human tumor peptide antigens and an equal number of non-antigenic human peptides to develop models for immunogenicity prediction. These methods encompassed k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The models underwent validation through internal cross-validation within 10 groups from the training set and were further assessed using an external test set. Remarkably, the kNN model demonstrated superior performance, recognizing 90% of the known immunogens in the test set. The RF model excelled in the identification of non-immunogens, accurately classifying 93% of them in the test set. The three top-performing ML models according to multiple evaluation metrics (SVM, RF, and XGBoost) are to be subsequently integrated into the new version of the VaxiJen server, facilitating tumor antigen prediction through a majority voting mechanism.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection

Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Early detection of bark beetle infestation using UAV-borne multispectral imagery: a case study on the spruce forest in the Czech Republic

Over the last decade, biotic disturbances caused by bark beetles have represented a serious environmental and economic issue in Central Europe. Great efforts are expended on the early detection and management of bark beetle infestation. Our study analyses a time series of UAV-borne multispectral imagery of a 250-ha forest in the Vysočina region in the Czech Republic. The study site represents a typical European spruce forest with routine silvicultural management. UAV-borne data was acquired three times during the vegetation period, specifically (a) before swarming, (b) at the early stage of infestation, and (c) in the post-abandon phase, i.e., after most bark beetle offspring left the trees. The spectral reflectance values and vegetation indices calculated from orthorectified and radiometrically calibrated imageries were statistically analyzed by quadratic discriminant analysis (QDA). The study shows that healthy and infested trees could be distinguished at the early stage of infestation, especially using NIR-related vegetation indices (NDVI and BNDVI in our case). Detecting infested trees is more significant by vegetation indices than spectral bands and increases with the increasing time after infestation. The study verified the usability of UAV-borne multispectral imageries for early detection of bark beetle infestation at the level of individual trees. Thus, these methods can contribute to precise and effective forest management on a local level.

Classification of Cognitive Impairment Using Quadratic Discriminant Analysis Based Spiral Dynamic Optimization Algorithm

Classification of Cognitive Impairment Using Quadratic Discriminant Analysis Based Spiral Dynamic Optimization Algorithm

Grading the Dominant Pathological Indices in Liver Diseases from Pathological Images Using Radiomics Methods

Purpose: This study aims to diagnose the severity of important pathological indices, i.e., fibrosis, steatosis, lobular inflammation, and ballooning from the pathological images of the liver tissue based on extracted features by radiomics methods.
 Materials and Methods: This research uses the pathological images obtained from liver tissue samples for 258 laboratory mice. After preprocessing the images and data augmentation, a collection of texture feature sets extracted by gray-level-based algorithms, including Global, Gray-level Co-Occurrence Matrix (GLCM), Gray-level Run length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM) algorithms. Then, advanced methods of classification, namely Support Vector Machine (SVM), Random Forest (RF), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Multi-layer Perceptrons (MLP) are employed. This procedure is provided separately for each of the four indices of fibrosis level in 6 grading classes, steatosis in 5 grading classes, inflammation in 4 grading classes, and ballooning in 3 grading classes. For a comparison of the output of these algorithms, the accuracy value obtained from the evaluation data is presented for the performance of different methods.
 Results: The results showed that, compared to other methods, the Gaussian SVM algorithm provides a better response to the classification of the grading of liver disease among all the indices from the pathological images due to its structural features. This value of accuracy was calculated at 84.30% for fibrosis, 90.55% for steatosis, 81.11% for inflammation, and 95.98% for ballooning.
 Conclusion: This fully automatic framework based on advanced radiomics algorithms and machine learning from pathological images can be very useful in clinical procedures and be considered as an assistant or a substitute for pathologists’ diagnoses.

MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

An Intelligent Model for Predictions of PIWI-Interacting RNAs and Their Functions

A documented class of short non-coding RNA molecules is known as PIWI interacting RNA (PiRNA). The creation of new drugs and the identification of various tumor types are linked to the PiRNA molecules. Additionally, it is related to controlling transcription of genes, squelching transposons, and preserving genomic stability. The discovery of piRNAs and their functionality has grown to be a significant research topic in bioinformatics because of the crucial influence that piRNAs play in biological processes. The 2L-piRNA-ML predictor is a strong two-layer predictor that is suggested in this research to enhance the prediction of PiRNA and their functionality. The suggested model uses Quadratic Discriminant Analysis Classifier, Linear Discriminant Analysis, Passive Aggressive Classifier, Extra Tree Classifier, Logistic Regression, Random Forest, Ridge Classifier CV for classification. It also employs DNC and TNC for extraction of features. The suggested model is created using a two-layer construction strategy. The 1st layer makes a prediction about a given sequence whether it is PiRNA or not, and the 2nd layer makes a prediction about a given PiRNA sequence whether it is having the function of instructing target mRNA deadenylation or not. Proposed model achieved 95.65 % accuracy at the first layer and 92.30% accuracy at the second layer.

Effects of feature reduction on emotion recognition using EEG signals and machine learning

AbstractElectroencephalography is a core technology of brain computer interfaces. Even a few number of electrodes can produce complex signals that are difficult to interpret. This is particularly true when trying to detect complex mental states, such as the identification of human emotions. This work analyzes the impact of feature reduction using the SJTU Emotion EEG data set, considering inter‐subject and inter‐session EEG classification. It is well established that EEG data tends to be subject dependent, and that the brain patterns produced by a person in response to a specific stimuli will tend to change over their lifetime, we can say that the above classification task is one of the most challenging test scenarios. We extract 1068 time and frequency domain features, single and multiple channels. Feature reduction was analyzed using both filter and wrapper based approaches. In the first group we use principal component analysis, Kernel PCA and Locally Linear Embedding, and in the second three meta‐heuristic algorithms are employed. For classification, we apply multi‐layer perceptron, quadratic discriminant analysis, random forest and support vector machines. Results show up to 100% accuracy on the median test error for some splits in the 10‐fold cross‐validation process. Best results were achieved by filter‐based selection, with the best average accuracy reached by Random Forest (93.20%) and Multi‐Layer Perceptron (92.71%), using components found with Kernel PCA. To the best of our knowledge, these classification results are the best ever achieved for the SEED data set on the subject‐independent and session‐independent problem. Comparisons using a Leave‐One‐Subject‐Out cross‐validation setting shows that the proposed methodology achieves state‐of‐the‐art results using only 12 electrodes. These off‐the‐shelf ML techniques, coupled with filter based feature reduction, achieve the best results, outperforming complex classification models and wrapper based meta‐heuristics.

Spectral Signatures of Macroalgae on Hawaiian Reefs

In Hawaiʻi, native macroalgae or “limu” are of ecological, cultural, and economic value. Invasive algae threaten native macroalgae and coral, which serve a key role in the reef ecosystem. Spectroscopy can be a valuable tool for species discrimination, while simultaneously providing insight into chemical processes occurring within photosynthetic organisms. The spectral identity and separability of Hawaiian macroalgal taxonomic groups and invasive and native macroalgae are poorly known and thus were the focus of this study. A macroalgal spectroscopic library of 30 species and species complexes found in Hawaiʻi was created. Spectral reflectance signatures were aligned with known absorption bands of taxonomic division-specific photosynthetic pigments. Quadratic discriminant analysis was used to explore if taxonomic groups of algae and native versus invasive algae could be classified spectrally. Algae were correctly classified based on taxonomic divisions 96.5% of the time and by species 83.2% of the time. Invasive versus native algae were correctly classified at a rate of 93% and higher, although the number of invasive algal species tested was limited. Analyses suggest that there is promise for the spectral separability of algae investigated in this study by algal taxonomic divisions and native-invasive status. This study created a spectral library that lays the groundwork for testing the spectral mapping of algae using current airborne and forthcoming spaceborne imaging spectroscopy, which could have significant implications for coastal management.

Energy efficient FPGA implementation of an epileptic seizure detection system using a QDA classifier

Epilepsy is a severe neurological disorder that causes seizures. It is detected by analyzing the electrical impulses of the human brain. Monitoring the brain is commonly done using an electroencephalogram (EEG). Seizure detection from the large recorded EEG dataset is a demanding task. However, numerous machine learning classifiers and the appropriate features can detect seizures. The Hjorth and statistical parameters are used in this study to provide an effective method for field programmable gate array (FPGA) realization of epileptic seizure detectors from EEG signals. Mobility, interquartile range (IQR), median absolute deviation (MAD), energy, non-linear energy, and simple square integral (SSI) are the various features analyzed in this work. The hardware architecture of the seizure detection system is captured in Verilog hardware descriptive language and realized on the Artix-7 FPGA. This paper presents three separate seizure detection models designed by pairing three different features with the Quadratic discriminant analysis (QDA) classifier. Among these three models, the energy and non-linear energy-based seizure detection system offers better performance than existing seizure detection systems because it possesses the lowest dynamic power consumption (0.116 µW), the highest design accuracy (99.4 %), and the highest sensitivity (100 %), which makes it the best seizure detection system for real-time applications.

Inkjet printer prediction under complicated printing conditions based on microscopic image features

A novel technique is introduced to predict the printer model used to produce a given document. Samples containing only a few letters printed under varying conditions (i.e., different printing modes, letter types, fonts) were collected to establish a dataset of 41 inkjet printer models from common manufacturers, such as HP, Canon, and Epson. Morphological features were analyzed by extraction of image features using several algorithms in a series of microscopic images and a Wilcoxon test was used to measure the significance of variations between printed samples. Significant differences between various printing conditions might post potential challenge to questioned document examination. Discriminant analysis and the k-nearest neighbor (KNN) algorithm were also employed for source printer prediction under varying printing condition on 30% images with the rest images as training dataset. The results of a validation experiment demonstrated that while quadratic discriminant analysis (QDA) achieved an accuracy of 96.3%, a combination of KNN and QDA reached 98.6%. As such, this technique could aid in the forensic examination of printed documents.

Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy

The diversity of fungal strains is influenced by genetic and environmental factors, growth conditions and mycelium age, and the spectral features of fungal mycelia are associated with their biochemical, physiological, and structural traits. This study investigates whether intraspecific differences can be detected in two closely related entomopathogenic species, namely Cordyceps farinosa and Cordyceps fumosorosea, using ultraviolet A to shortwave infrared (UVA-SWIR) reflectance spectra. Phylogenetic analysis of all strains revealed a high degree of uniformity among the populations of both species. The characteristics resulting from variation in the species, as well as those resulting from the age of the cultures were determined. We cultured fungi on PDA medium and measured the reflectance of mycelia in the 350–2500 nm range after 10 and 17 days. We subjected the measurements to quadratic discriminant analysis (QDA) to identify the minimum number of bands containing meaningful information. We found that when the age of the fungal culture was known, species represented by a group of different strains could be distinguished with no more than 3–4 wavelengths, compared to 7–8 wavelengths when the age of the culture was unknown. At least 6–8 bands were required to distinguish cultures of a known species among different age groups. Distinguishing all strains within a species was more demanding: at least 10 bands were required for C. fumosorosea and 21 bands for C. farinosa. In conclusion, fungal differentiation using point reflectance spectroscopy gives reliable results when intraspecific and age variations are taken into account.

Classification of the machine state in turning processes by using the acoustic emission

Processing digital information stands as a crucial foundation of Industry 4.0, facilitating a spectrum of activities from monitoring processes to their understanding and optimization. The application of data processing techniques, including feature extraction and classification, coupled with the identification of the most suitable features for specific purposes, continues to pose a significant challenge in the manufacturing sector. This research investigates the suitability of classification methods for machine and tool state classification by employing acoustic emission (AE) sensors during the dry turning of Ti6Al4V. Features such as quantiles, Fourier coefficients, and mel-frequency cepstral coefficients are extracted from the AE signals to facilitate classification. From this features the 20 best are selected for the classification to reduce the dimension of the feature space and redundancy. Algorithms including decision tree, k-nearest-neighbors (KNN), and quadratic discriminant analysis (QDA) are tested for the classification of machine states. Of these, QDA exhibits the highest accuracy at 98.6 %. Nonetheless, an examination of the confusion matrix reveals that certain classes, influenced by imbalanced training data, exhibit a lower prediction accuracy. In summary, the study affirms the potential of AE sensors for machine state recognition and tool condition monitoring. Although QDA emerges as the most acurate classifier, there remains an avenue for refinement, particularly in training data optimization and decision-making processes, to augment accuracy.

Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules.

Thyroid nodules are common clinical entities, with a significant proportion being malignant. Early, accurate, and non-invasive tools to differentiate benign and malignant nodules can optimize patient management and reduce unnecessary surgery. This study aimed to evaluate the efficacy and accuracy of near-infrared spectroscopy (NIRS) in distinguishing benign from malignant thyroid nodules. A diffuse reflectance spectrum for a total of 20 thyroid nodule samples (10 samples as colloid goiter and 10 samples as thyroid cancer), were acquired in the wavelength range from 1000 to 2500 nm. Spectral data from NIRS were analyzed by means of principal component analysis (PCA), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) to classify and differentiate thyroid nodule samples. The present study found that NIRS effectively distinguished colloid goiter and thyroid cancer using the first two principal components (PCs), explaining 90% and 10% of the variance, respectively. QDA discrimination plot displayed a clear separation between colloid goiter and thyroid cancer with minimal overlap, aligning with reported 95% accuracy. Additionally, applying LDA to seven PCs from PCA achieved a 100% accuracy rate in classifying colloid goiter and thyroid cancer from near-infrared spectral data. In conclusion, NIRS offers a promising, non-invasive complementing diagnostic tool for differentiating benign from malignant thyroid nodules with high accuracy. Future work should integrate these results into predictive model development, emphasizing external validation, alternative performance metrics, and protecting against potential overfitting translation of a machine learning model to a clinical setting.

Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

This paper proposes a deep learning method to predict cancer risk from gene symbols using a multilayer perceptron (MLP) feed forward neural network. The paper uses a data set of gene symbols and their corresponding cancer risk labels, obtained from a DNA microarray analysis. The paper then builds and compares different machine learning models, such as logistic regression, linear discriminant analysis, quadratic discriminant analysis, decision tree classifier, gaussian nb, ada boost classifier, gaussian process classifier, support vector machine, and random forest. Deep learning MLP model is built, tuned and optimized for hyperparameters which improves the accuracy significantly by 9.09% compared to the best machine learning model. The paper evaluates the performance of the MLP model on the data set using accuracy, precision, recall, and F1-score metrics. This paper contributes to the field of machine learning and bioinformatics by providing a novel and effective way to predict cancer risk from gene symbols.

Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception.

Industrial robots are the main piece of equipment of intelligent manufacturing, and array-type tactile sensors are considered to be the core devices for their active sensing and understanding of the production environment. A great challenge for existing array-type tactile sensors is the wiring of sensing units in a limited area, the contradiction between a small number of sensing units and high resolution, and the deviation of the overall output pattern due to the difference in the performance of each sensing unit itself. Inspired by the human somatosensory processing hierarchy, we combine tactile sensors with artificial intelligence algorithms to simplify the sensor architecture while achieving tactile resolution capabilities far greater than the number of signal channels. The prepared 8-electrode carbon-based conductive network achieves high-precision identification of 32 regions with 97% classification accuracy assisted by a quadratic discriminant analysis algorithm. Notably, the output of the sensor remains unchanged after 13,000 cycles at 60 kPa, indicating its excellent durability performance. Moreover, the large-area skin-like continuous conductive network is simple to fabricate, cost-effective, and can be easily scaled up/down depending on the application. This work may address the increasing need for simple fabrication, rapid integration, and adaptable geometry tactile sensors for use in industrial robots.

Water Quality Prediction using Statistical, Ensemble and Hybrid models

<p>Water is an essential elixir for several living organisms to function and survive. But it gets contaminated through several sources such as industrial wastes, oil spills, marine dumping, etc. With a growing population, availability of good quality water is of grave importance. This has become the motivation to probe into analysis of water quality from the outcomes of Statistical and Ensemble methods and to find the best working models from both methods. Research has been done to predict water quality analysis using standalone statistical and ensemble models. So, this research focuses on obtaining the best Statistical and Ensemble model separately among the models tried. The statistical models implemented for comparison are Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA). The Ensemble models used are Bagging, Boosting and Stacking. The models are then combined to build a Hybrid model to observe the comparisons between the three. The performance metrics used are Confusion Matrix, Accuracy, Precision, Recall, F1-score and ROC curve. While comparing the models, it is observed that Hybrid model produces the most accurate results, hence proving that the combination of Statistical and Ensemble model is efficient.</p>

Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting

Objectives: Over the past few years, there prevails an abundance wealth of big data obtained via patients' electronic health records. One of the leading causes of mortality globally is the cardiovascular disease. Based on the present test and history cardiovascular disease diagnosing of patients can be done. Therefore, early and quick diagnosis can reduce the mortality rate. To address their needs, several machine learning methods have been employed in the recent past in cardiovascular disease diagnosis and prediction. Previous research was also concentrated on acquiring the significant features to heart disease prediction however less importance was given to the time involved and error rate to identifying the strength of these features. Methods: In this work we plan to develop a method called, Regularized Principal Component and Quadratic Weighted Entropy Boosting (RPC-QWEB) for predicting heart disease. Initially in RPC-QWEB, relevant features are selected to avoid missing values in the input database by employing Regularized Principal Component Regressive Feature Selection (RPCRFS). Second, with the obtained dimensionality reduced features, Quadratic Weighted Entropy Boosting Classification (QWEBC) process is carried out to classify the patient data as normal or abnormal. The QWEBC process is an ensemble of several weak classifiers (i.e., Quadratic Classifier). The weak classifier results are combined to form strong classifier and provide final prediction results as normal or abnormal condition with minimal error rate. Findings: Experimental evaluation is carried out on factors with the cardiovascular disease dataset such as heart disease prediction accuracy, heart disease prediction time, sensitivity, error rate with respect to distinct numbers of patient data. The proposed RPC-QWEB method was compared with existing Heart Disease Prediction Framework (HDPF) and Swarm Artificial Neural Network (Swarm-ANN). Novelty: RPC-QWEB method outperforms the conventional learning methods in terms of numerous performance matrices. The RPC-QWEB method produces 3% and 5% increase in terms of accuracy and sensitivity and 7% and 29% reduced prediction time and error rate as compared to the existing benchmark methods. We may use this method to predict the heart disease at early stage there by we can reduce the death rate. Keywords: Big data, Regularized Principal Component, Quadratic Weighted Entropy Boosting, Regressive Feature Selection, Classification