Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.publishedVersio

Multi-Criteria Inventory Classification and Root Cause Analysis Based on Logical Analysis of Data

RÉSUMÉ : La gestion des stocks de pièces de rechange donne un avantage concurrentiel vital dans de nombreuses industries, en passant par les entreprises à forte intensité capitalistique aux entreprises de service. En raison de la quantité élevée d'unités de gestion des stocks (UGS) distinctes, il est presque impossible de contrôler les stocks sur une base unitaire ou de porter la même attention à toutes les pièces. La gestion des stocks de pièces de rechange implique plusieurs intervenants soit les fabricants d'équipement d'origine (FEO), les distributeurs et les clients finaux, ce qui rend la gestion encore plus complexe. Des pièces de rechange critiques mal classées et les ruptures de stocks de pièces critiques ont des conséquences graves. Par conséquent il est essentiel de classifier les stocks de pièces de rechange dans des classes appropriées et d'employer des stratégies de contrôle conformes aux classes respectives. Une classification ABC et certaines techniques de contrôle des stocks sont souvent appliquées pour faciliter la gestion UGS. La gestion des stocks de pièces de rechange a pour but de fournir des pièces de rechange au moment opportun. La classification des pièces de rechange dans des classes de priorité ou de criticité est le fondement même de la gestion à grande échelle d’un assortiment très varié de pièces. L'objectif de la classification est de classer systématiquement les pièces de rechange en différentes classes et ce en fonction de la similitude des pièces tout en considérant leurs caractéristiques exposées sous forme d'attributs. L'analyse ABC traditionnelle basée sur le principe de Pareto est l'une des techniques les plus couramment utilisées pour la classification. Elle se concentre exclusivement sur la valeur annuelle en dollar et néglige d'autres facteurs importants tels que la fiabilité, les délais et la criticité. Par conséquent l’approche multicritères de classification de l'inventaire (MCIC) est nécessaire afin de répondre à ces exigences. Nous proposons une technique d'apprentissage machine automatique et l'analyse logique des données (LAD) pour la classification des stocks de pièces de rechange. Le but de cette étude est d'étendre la méthode classique de classification ABC en utilisant une approche MCIC. Profitant de la supériorité du LAD dans les modèles de transparence et de fiabilité, nous utilisons deux exemples numériques pour évaluer l'utilisation potentielle du LAD afin de détecter des contradictions dans la classification de l'inventaire et de la capacité sur MCIC. Les deux expériences numériques ont démontré que LAD est non seulement capable de classer les stocks mais aussi de détecter et de corriger les observations contradictoires en combinant l’analyse des causes (RCA). La précision du test a été potentiellement amélioré, non seulement par l’utilisation du LAD, mais aussi par d'autres techniques de classification d'apprentissage machine automatique tels que : les réseaux de neurones (ANN), les machines à vecteurs de support (SVM), des k-plus proches voisins (KNN) et Naïve Bayes (NB). Enfin, nous procédons à une analyse statistique afin de confirmer l'amélioration significative de la précision du test pour les nouveaux jeux de données (corrections par LAD) en comparaison aux données d'origine. Ce qui s’avère vrai pour les cinq techniques de classification. Les résultats de l’analyse statistique montrent qu'il n'y a pas eu de différence significative dans la précision du test quant aux cinq techniques de classification utilisées, en comparant les données d’origine avec les nouveaux jeux de données des deux inventaires.----------ABSTRACT : Spare parts inventory management plays a vital role in maintaining competitive advantages in many industries, from capital intensive companies to service networks. Due to the massive quantity of distinct Stock Keeping Units (SKUs), it is almost impossible to control inventory by individual item or pay the same attention to all items. Spare parts inventory management involves all parties, from Original Equipment Manufacturer (OEM), to distributors and end customers, which makes this management even more challenging. Wrongly classified critical spare parts and the unavailability of those critical items could have severe consequences. Therefore, it is crucial to classify inventory items into classes and employ appropriate control policies conforming to the respective classes. An ABC classification and certain inventory control techniques are often applied to facilitate SKU management. Spare parts inventory management intends to provide the right spare parts at the right time. The classification of spare parts into priority or critical classes is the foundation for managing a large-scale and highly diverse assortment of parts. The purpose of classification is to consistently classify spare parts into different classes based on the similarity of items with respect to their characteristics, which are exhibited as attributes. The traditional ABC analysis, based on Pareto's Principle, is one of the most widely used techniques for classification, which concentrates exclusively on annual dollar usage and overlooks other important factors such as reliability, lead time, and criticality. Therefore, multi-criteria inventory classification (MCIC) methods are required to meet these demands. We propose a pattern-based machine learning technique, the Logical Analysis of Data (LAD), for spare parts inventory classification. The purpose of this study is to expand the classical ABC classification method by using a MCIC approach. Benefiting from the superiority of LAD in pattern transparency and robustness, we use two numerical examples to investigate LAD’s potential usage for detecting inconsistencies in inventory classification and the capability on MCIC. The two numerical experiments have demonstrated that LAD is not only capable of classifying inventory, but also for detecting and correcting inconsistent observations by combining it with the Root Cause Analysis (RCA) procedure. Test accuracy improves potentially not only with the LAD technique, but also with other major machine learning classification techniques, namely artificial neural network (ANN), support vector machines (SVM), k-nearest neighbours (KNN) and Naïve Bayes (NB). Finally, we conduct a statistical analysis to confirm the significant improvement in test accuracy for new datasets (corrections by LAD) compared to original datasets. This is true for all five classification techniques. The results of statistical tests demonstrate that there is no significant difference in test accuracy in five machine learning techniques, either in the original or the new datasets of both inventories

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management