Intrinsically Interpretable Machine Learning In Computer Aided Diagnosis

This thesis presents a set of intrinsically interpretable machine learning models which were applied on real-world medical datasets, a synthetic dataset, and a publicly available dataset from the UCI repository, which posed the challenges of heterogeneous measurements, imbalanced classes, and systematic missingness. The interpretability of the presented set of classifiers are in terms of (1) the classifier's confidence in assigning a class label to a presented sample (instead of just crisp labels), (2) straightforward visualization of the decision boundaries of a presented problem as learned by the classifier, (3) implicit feature relevance computation, and (4) extraction of typical profile(s) of each of the learned classes (prototypes) by the classifier. These newly introduced set of classifiers are nearest prototype based classifiers (NPCs) which belong to the family of Learning Vector Quantization (LVQ). This thesis first presents the angle-dissimilarity based variants of Generalized Relevance LVQ (GRLVQ), Generalized Matrix Relevance LVQ (GMLVQ), Local metric tensor LVQ (LGMLVQ) and Localized Limited Rank Metric LVQ (LLiRAM LVQ). Next, probabilistic variants of the GMLVQ and angle GMLVQ are presented. These newly developed models not just have comparable performance to that of Random Forests, they also help in medical knowledge-extraction from the dataset they are trained on. In this thesis we introduced a geodesic averaging technique which combined the power of ensembling while maintaining the interpretability aspect of the LVQ models

Visualization and knowledge discovery from interpretable models

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). So, in this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem, and visualisation of the classifier and decision boundaries: angle based variants of Learning Vector Quantization. The performance of the developed classifiers were comparable to those reported in literature for UCI’s heart disease dataset treated as a binary class problem. The newly developed classifiers also helped investigating the complexities of this dataset as a multiclass proble

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the transparency of these models increase trust among application domain experts. Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques. In this paper we present a family of prototype-based (PB) interpretable models which are capable of handling these issues. The models introduced in this contribution show comparable or superior performance to alternative techniques applicable in such situations. However, unlike ensemble based models, which have to compromise on easy interpretation, the PB models here do not. Moreover we propose a strategy of harnessing the power of ensembles while maintaining the intrinsic interpretability of the PB models, by averaging the model parameter manifolds. All the models were evaluated on a synthetic (publicly available dataset) in addition to detailed analyses of two real-world medical datasets (one publicly available). Results indicated that the models and strategies we introduced addressed the challenges of real-world medical data, while remaining computationally inexpensive and transparent, as well as similar or superior in performance compared to their alternatives

Positive and Negative Parenting in Conduct Disorder with High versus Low Levels of Callous-Unemotional Traits

Less is known about the relationship between conduct disorder (CD), callous-unemotional (CU) traits, and positive and negative parenting in youth compared to early childhood. We combined traditional univariate analyses with a novel machine learning classifier (Angle-based Generalized Matrix Learning Vector Quantization) to classify youth (N = 756; 9-18 years) into typically developing (TD) or CD groups with or without elevated CU traits (CD/HCU, CD/LCU, respectively) using youth- A nd parent-reports of parenting behavior. At the group level, both CD/HCU and CD/LCU were associated with high negative and low positive parenting relative to TD. However, only positive parenting differed between the CD/HCU and CD/LCU groups. In classification analyses, performance was best when distinguishing CD/HCU from TD groups and poorest when distinguishing CD/HCU from CD/LCU groups. Positive and negative parenting were both relevant when distinguishing CD/HCU from TD, negative parenting was most relevant when distinguishing between CD/LCU and TD, and positive parenting was most relevant when distinguishing CD/HCU from CD/LCU groups. These findings suggest that while positive parenting distinguishes between CD/HCU and CD/LCU, negative parenting is associated with both CD subtypes. These results highlight the importance of considering multiple parenting behaviors in CD with varying levels of CU traits in late childhood/adolescence

Bacterial Biopolymer: Its Role in Pathogenesis to Effective Biomaterials

Bacteria are considered as the major cell factories, which can effectively convert nitrogen and carbon sources to a wide variety of extracellular and intracellular biopolymers like polyamides, polysaccharides, polyphosphates, polyesters, proteinaceous compounds, and extracellular DNA. Bacterial biopolymers find applications in pathogenicity, and their diverse materialistic and chemical properties make them suitable to be used in medicinal industries. When these biopolymer compounds are obtained from pathogenic bacteria, they serve as important virulence factors, but when they are produced by non-pathogenic bacteria, they act as food components or biomaterials. There have been interdisciplinary studies going on to focus on the molecular mechanism of synthesis of bacterial biopolymers and identification of new targets for antimicrobial drugs, utilizing synthetic biology for designing and production of innovative biomaterials. This review sheds light on the mechanism of synthesis of bacterial biopolymers and its necessary modifications to be used as cell based micro-factories for the production of tailor-made biomaterials for high-end applications and their role in pathogenesis