Cardiovascular diseases (CVDs) continue to be a prominent cause of global mortality, necessitating the development of effective risk prediction models to combat the rise in heart disease (HD) mortality rates. This work presents a novel dual-stage stacked machine learning (ML) based computational risk prediction model for cardiac disorders. Leveraging a dataset that includes eleven significant characteristics from 1190 patients from five distinct sources, five ML classifiers are utilized to create the initial prediction model. To ensure robustness and generalizability, the classifiers are cross-validated ten times. The model performance is optimized by employing two hyperparameter tuning approaches: RandomizedSearchCV and GridSearchCV. These methods aim to find the optimal estimator values. The highest-performing models, specifically Random Forest, Extreme Gradient Boost, and Decision Tree undergo additional refinement using a stacking ensemble technique. The stacking model, which leverages the capabilities of the three models, attains a remarkable accuracy rate of 96%, a recall value of 0.98, and a ROC-AUC score of 0.96. Notably, the rate of false-negative results is below 1%, demonstrating a high level of accuracy and a non-overfitted model. To evaluate the model’s stability and repeatability, a comparable dataset consisting of 1000 occurrences is employed. The model consistently achieves an accuracy of 96.88% under identical experimental settings. This highlights the strength and dependability of the suggested computer model for predicting the risk of cardiac illnesses. The outcomes indicate that employing this two-step stacking ML method shows potential for prompt and precise diagnosis, hence aiding the worldwide endeavor to decrease fatalities caused by heart disease.

In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN) and Adaptive Neuro-fuzzy Inference System (ANFIS). We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms

The breast cancer is a severe disease found among females all over the world. This is a type of cancer disease arising from human breast tissue cells, usually from the lobules or the inner lining of the milk ducts that provide the ducts with milk. A recent medical survey reveals that throughout the world breast cancer occurs in 22.9% of all cancers in women and it also causes 13.7% of cancer deaths in them. Breast cancer, being very harmful to all women, may cause loss of breasts or may even cost their life. Diagnosis of breast cancer disease is an important area of data mining research. In our work, different classification techniques are applied on the benchmark Breast Cancer Wisconsin dataset from the UCI machine language repository for detection of breast cancer. Principal component analysis (PCA) technique has been used to reduce the dimension of the dataset. Our objectives is to diagnose and analyze breast cancer disease with the help of two well-known classifiers, namely, MLP using Backpropagation NN (MLP BPN) and Support Vector Machine (SVM) and, thereafter assess their performance in terms of different performance measures like Accuracy, Precision, Recall, F-Measure, Kappa statistic etc.

In this paper, we propose a neuro-fuzzy (NF) classification technique to determine various soil classes from large imagery soil databases. The technique looks at the feature-wise degree of belongings of the imagery databases to obtainable soil classes using a fuzzification method. The fuzzification method builds a membership matrix with an element count equal to the mathematical product of the number of data records and soil classes present. The elements of this matrix are the input to a neural network model. We apply our technique to three UCI databases, namely, Statlog Landsat Satellite, Forest Covertype, and Wilt for soil classification. The paper aims to find out soil classes using the proposed technique, and then compare its performance with four well-known classification algorithms, namely, radial basis function network, k-nearest neighbor, support vector machine, and adaptive NF inference system. Numerous measures, for example, root-mean-square error, kappa statistic, accuracy, false positive rate, true positive rate, precision, recall, F-measure, and area under the curve, are used for evaluating the quantitative analysis of the simulated results. All these evaluation measures approve the supremacy of the proposed NF method.

Weather Data Mining is a form of Data mining concerned with finding hidden patterns inside largely available meteorological data, so that the information retrieved can be transformed into usable knowledge. A variety of data mining tools and techniques are available in the industry, but they have been used in a very limited way for meteorological data. In this paper, a neural network-based algorithm for predicting the atmosphere for a future time and a given location is presented. We have used Back Propagation Neural (BPN) Network for initial modelling. The results obtained by BPN model are fed to a Hopfield Network. The performance of our proposed ANN-based method (BPN and Hopfield Network based combined approach) tested on 3 years weather data set comprising 15000 records containing attributes like temperature, humidity and wind speed. The prediction error is found to be very less and the learning converges very sharply. The main focus of this paper is based on predictive data mining by which we can extract interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of meteorological data.